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  • 1.
    Aalto, Anne
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet.
    Dahlqvist Leinhard, Olof
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Jaworski, M
    Gustavsson, M
    Tisell, Anders
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Hälsouniversitetet.
    Landtblom, Anne-Marie
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Psykiatri. Östergötlands Läns Landsting, Sinnescentrum, Neurokirurgiska kliniken US. Linköpings universitet, Hälsouniversitetet.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Effects of Betainterferon treatment in Multiple Sclerosis Studied by Quantitative 1H MRS2009Konferansepaper (Annet vitenskapelig)
  • 2.
    Aalto, Anne
    et al.
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Sjoewall, Johanna
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Klinisk immunologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Medicincentrum, Infektionskliniken i Östergötland.
    Davidsson, Leif
    Linköpings universitet, Institutionen för medicin och vård, Radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Forsberg, Pia
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Infektionsmedicin. Östergötlands Läns Landsting, Medicincentrum, Infektionskliniken i Östergötland.
    Smedby, Örjan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Brain magnetic resonance imaging does not contribute to the diagnosis of chronic neuroborreliosis2007Inngår i: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 48, nr 7, s. 755-762Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Borrelia infections, especially chronic neuroborreliosis ( NB), may cause considerable diagnostic problems. This diagnosis is based on symptoms and findings in the cerebrospinal fluid but is not always conclusive. Purpose: To evaluate brain magnetic resonance imaging ( MRI) in chronic NB, to compare the findings with healthy controls, and to correlate MRI findings with disease duration. Material and Methods: Sixteen well- characterized patients with chronic NB and 16 matched controls were examined in a 1.5T scanner with a standard head coil. T1- ( with and without gadolinium), T2-, and diffusion- weighted imaging plus fluid- attenuated inversion recovery ( FLAIR) imaging were used. Results: White matter lesions and lesions in the basal ganglia were seen in 12 patients and 10 controls ( no significant difference). Subependymal lesions were detected in patients down to the age of 25 and in the controls down to the age of 43. The number of lesions was correlated to age both in patients ( rho=0.83, P < 0.01) and in controls ( rho=0.61, P < 0.05), but not to the duration of disease. Most lesions were detected with FLAIR, but many also with T2- weighted imaging. Conclusion: A number of MRI findings were detected in patients with chronic NB, although the findings were unspecific when compared with matched controls and did not correlate with disease duration. However, subependymal lesions may constitute a potential finding in chronic NB.

  • 3. Ahlström, H.
    et al.
    Johansson, L.
    Smedby, Örjan
    Uppsala University.
    Magnusson, A.
    Raland, H.
    Wahlberg, J.
    Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) in the diagnosis of renal transplant artery stenosis1996Konferansepaper (Fagfellevurdert)
  • 4. Ahlström, H.
    et al.
    Smedby, Örjan
    Uppsala University.
    Löfberg, AM.
    Bergkvist, D.
    Ljungman, C.
    Magnetic Resonance Angiography of Peripheral Run-off Vessels1995Konferansepaper (Annet vitenskapelig)
  • 5. Andersson, Malin
    et al.
    Jägervall, Karl
    Eriksson, Per
    Persson, Anders
    Granerus, Göran
    Wang, Chunliang
    Linköping Univ, Sweden.
    Smedby, Örjan
    Linköping Univ, Sweden.
    How to measure renal artery stenosis - a retrospective comparison of morphological measurement approaches in relation to hemodynamic significance2015Inngår i: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, artikkel-id 42Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Although it is well known that renal artery stenosis may cause renovascular hypertension, it is unclear how the degree of stenosis should best be measured in morphological images. The aim of this study was to determine which morphological measures from Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) are best in predicting whether a renal artery stenosis is hemodynamically significant or not. Methods: Forty-seven patients with hypertension and a clinical suspicion of renovascular hypertension were examined with CTA, MRA, captopril-enhanced renography (CER) and captopril test (Ctest). CTA and MRA images of the renal arteries were analyzed by two readers using interactive vessel segmentation software. The measures included minimum diameter, minimum area, diameter reduction and area reduction. In addition, two radiologists visually judged the diameter reduction without automated segmentation. The results were then compared using limits of agreement and intra-class correlation, and correlated with the results from CER combined with Ctest (which were used as standard of reference) using receiver operating characteristics (ROC) analysis. Results: A total of 68 kidneys had all three investigations (CTA, MRA and CER + Ctest), where 11 kidneys (16.2 %) got a positive result on the CER + Ctest. The greatest area under ROC curve (AUROC) was found for the area reduction on MRA, with a value of 0.91 (95 % confidence interval 0.82-0.99), excluding accessory renal arteries. As comparison, the AUROC for the radiologists' visual assessments on CTA and MRA were 0.90 (0.82-0.98) and 0.91 (0.83-0.99) respectively. None of the differences were statistically significant. Conclusions: No significant differences were found between the morphological measures in their ability to predict hemodynamically significant stenosis, but a tendency of MRA having higher AUROC than CTA. There was no significant difference between measurements made by the radiologists and measurements made with fuzzy connectedness segmentation. Further studies are required to definitely identify the optimal measurement approach.

  • 6.
    Andersson, Malin
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Medicinska fakulteten.
    Jägervall, Karl
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Eriksson, Per
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för neuro- och inflammationsvetenskap. Region Östergötland, Hjärt- och Medicincentrum, Reumatologiska kliniken i Östergötland. Linköpings universitet, Medicinska fakulteten.
    Persson, Anders
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Granerus, Göran
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Fysiologiska kliniken US.
    Wang, Chunliang
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. KTH Royal Institute Technology, Sweden.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. KTH Royal Institute Technology, Sweden.
    How to measure renal artery stenosis - a retrospective comparison of morphological measurement approaches in relation to hemodynamic significance2015Inngår i: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, nr 42Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Although it is well known that renal artery stenosis may cause renovascular hypertension, it is unclear how the degree of stenosis should best be measured in morphological images. The aim of this study was to determine which morphological measures from Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) are best in predicting whether a renal artery stenosis is hemodynamically significant or not. Methods: Forty-seven patients with hypertension and a clinical suspicion of renovascular hypertension were examined with CTA, MRA, captopril-enhanced renography (CER) and captopril test (Ctest). CTA and MRA images of the renal arteries were analyzed by two readers using interactive vessel segmentation software. The measures included minimum diameter, minimum area, diameter reduction and area reduction. In addition, two radiologists visually judged the diameter reduction without automated segmentation. The results were then compared using limits of agreement and intra-class correlation, and correlated with the results from CER combined with Ctest (which were used as standard of reference) using receiver operating characteristics (ROC) analysis. Results: A total of 68 kidneys had all three investigations (CTA, MRA and CER + Ctest), where 11 kidneys (16.2 %) got a positive result on the CER + Ctest. The greatest area under ROC curve (AUROC) was found for the area reduction on MRA, with a value of 0.91 (95 % confidence interval 0.82-0.99), excluding accessory renal arteries. As comparison, the AUROC for the radiologists visual assessments on CTA and MRA were 0.90 (0.82-0.98) and 0.91 (0.83-0.99) respectively. None of the differences were statistically significant. Conclusions: No significant differences were found between the morphological measures in their ability to predict hemodynamically significant stenosis, but a tendency of MRA having higher AUROC than CTA. There was no significant difference between measurements made by the radiologists and measurements made with fuzzy connectedness segmentation. Further studies are required to definitely identify the optimal measurement approach.

  • 7.
    Andersson, Mats
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Sandborg, Michael
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Farnebäck, Gunnar
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Hans, Knutsson
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Adaptiv filtering of 4D-heart CT for image denoising and patient safety2010Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The aim of this medical image science project is to increase patient safety in terms of improved image quality and reduced exposure to ionizing radiation in CT. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes. Four-dimensional (4D) medical image data are captured as a time sequence of image volumes. During 4D image acquisition, a 3D image of the patient is recorded at regular time intervals. The resulting data will consequently have three spatial dimensions and one temporal dimension. Increasing the dimensionality of the data impose a major increase the computational demands. The initial linear filtering which is the cornerstone in all adaptive image enhancement algorithms increase exponentially with the dimensionality. On the other hand the potential gain in Signal to Noise Ratio (SNR) also increase exponentially with the dimensionality. This means that the same gain in noise reduction that can be attained by performing the adaptive filtering in 3D as opposed to 2D can be expected to occur once more by moving from 3D to 4D. The initial tests on on both synthetic and clinical 4D images has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. When tuning the parameters for adaptive filtering is extremely important to attain maximal diagnostic value which not necessarily coincide with an an eye pleasing image for a layman. Although this application focus on CT the resulting adaptive filtering methods will be beneficial for a wide range of 3D/4D medical imaging modalities e.g. shorter acquisition time in MRI and improved elimination of noise in 3D or 4D ultrasound datasets.

  • 8. Andersson, Thord
    et al.
    Romu, Thobias
    Karlsson, Anette
    Norén, Bengt
    Forsgren, Mikael F
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning. Linköping University.
    Kechagias, Stergios
    Almer, Sven
    Lundberg, Peter
    Borga, Magnus
    Leinhard, Olof Dahlqvist
    Consistent intensity inhomogeneity correction in water-fat MRI2015Inngår i: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, nr 2Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    PURPOSE: To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities

    METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS: CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION: CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type.

  • 9.
    Andersson, Thord
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Romu, Thobias
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Karlsson, Anette
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Norén, Bengt
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Forsgren, Mikael
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Kechagias, Stergios
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Magtarmmedicinska kliniken.
    Almer, Sven
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för neuro- och inflammationsvetenskap. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Magtarmmedicinska kliniken.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Dahlqvist Leinhard, Olof
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Consistent intensity inhomogeneity correction in water–fat MRI2015Inngår i: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, nr 2, s. 468-476Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    PURPOSE:

    To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS:

    CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION:

    CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.

  • 10.
    Andersson, Thord
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Romu, Thobias
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Norén, Bengt
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Forsgren, Mikael
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Almer, Sven
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Gastroenterologi och hepatologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Endokrinmedicinska kliniken.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Borga, Magnus
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Dahlqvist Leinhard, Olof
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA)2012Inngår i: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012, 2012Konferansepaper (Annet vitenskapelig)
  • 11.
    Andersson-Engels, Stefan
    et al.
    Inst för fysik Lunds Tekniska Högskola.
    Pålsson, S
    Backlund, Erik Olof
    IMT LiU.
    Sturnegk, Patrik
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för nervsystem och rörelseorgan, Neurokirurgi. Östergötlands Läns Landsting, Rekonstruktionscentrum, Neurokirurgiska kliniken US.
    Lundberg, Peter
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiofysik. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Svanberg, K
    Eriksson, Ola
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Biomedicinsk instrumentteknik.
    Wårdell, Karin
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Biomedicinsk instrumentteknik.
    ALA-PpIX Fluorescence and spectroscopy in connection with stereotactic biopsy of human glioblastomas2005Inngår i: European Conference on Biomedical Optics,2005, 2005Konferansepaper (Fagfellevurdert)
  • 12.
    Astaraki, Mehdi
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.
    Toma-Dasu, I.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.
    Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation2019Inngår i: 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Springer, 2019, Vol. 11769, s. 249-256Konferansepaper (Fagfellevurdert)
    Abstract [en]

    One of the major differences between medical doctor training and machine learning is that doctors are trained to recognize normal/healthy anatomy first. Knowing the healthy appearance of anatomy structures helps doctors to make better judgement when some abnormality shows up in an image. In this study, we propose a normal appearance autoencoder (NAA), that removes abnormalities from a diseased image. This autoencoder is semi-automatically trained using another partial convolutional in-paint network that is trained using healthy subjects only. The output of the autoencoder is then fed to a segmentation net in addition to the original input image, i.e. the latter gets both the diseased image and a simulated healthy image where the lesion is artificially removed. By getting access to knowledge of how the abnormal region is supposed to look, we hypothesized that the segmentation network could perform better than just being shown the original slice. We tested the proposed network on the LIDC-IDRI dataset for lung cancer detection and segmentation. The preliminary results show the NAA approach improved segmentation accuracy substantially in comparison with the conventional U-Net architecture.

  • 13.
    Astaraki, Mehdi
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Buizza, G.
    Toma-Dasu, I.
    Lazzeroni, M.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Early survival prediction in non-small cell lung cancer with PET/CT size aware longitudinal pattern2019Inngår i: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, ISSN 0167-8140, Vol. 133, s. S208-S209Artikkel i tidsskrift (Fagfellevurdert)
  • 14. Astaraki, Mehdi
    et al.
    Wang, Chunliang
    Buizza, Giulia
    Toma-Dasu, Iuliana
    Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Karolinska Institutet, Sweden.
    Lazzeroni, Marta
    Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Karolinska Institutet, Sweden.
    Smedby, Örjan
    Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method2019Inngår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, s. 58-65Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose

    To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.

    Methods

    Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC).

    Results

    The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values.

    Conclusion

    A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

  • 15.
    Astaraki, Mehdi
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning. Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Buizza, Giulia
    Politecn Milan, Dept Elect Informat & Bioengn, Piazza Leonardo da Vinci 42, I-20133 Milan, Italy..
    Toma-Dasu, Iuliana
    Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.;Stockholm Univ, Dept Phys, SE-10691 Stockholm, Sweden..
    Lazzeroni, Marta
    Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.;Stockholm Univ, Dept Phys, SE-10691 Stockholm, Sweden..
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method2019Inngår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, s. 58-65Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). Results: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROC(sALop) = 0.90 vs. AUROC(radiomic) = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. Conclusion: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

  • 16.
    Batool, Nazre
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Chowdhury, Manish
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Moreno, Rodrigo
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Estimation of trabecular bone thickness in gray scale: a validation study2017Inngår i: International Journal of Computer Assisted Radiology and Surgery, Vol. 12, nr Supplement 1Artikkel i tidsskrift (Fagfellevurdert)
  • 17.
    Bendazzoli, Simone
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.
    Brusini, Irene
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning. Karolinska Inst, Dept Neurobiol Care Sci & Soc, Alfred Nobels Alle 23,D3, S-14152 Huddinge, Sweden..
    Damberg, Peter
    Karolinska Inst, Dept Clin Neurosci, Tomtebodavagen 18A P1 5, S-17177 Stockholm, Sweden..
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Andersson, Leif
    Uppsala Univ, Dept Med Biochem & Microbiol, Sci Life Lab Uppsala, Biomedicinskt Ctr BMC, Husargatan 3, S-75237 Uppsala, Sweden..
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Automatic rat brain segmentation from MRI using statistical shape models and random forest2019Inngår i: MEDICAL IMAGING 2019: IMAGE PROCESSING / [ed] Angelini, ED Landman, BA, SPIE-INT SOC OPTICAL ENGINEERING , 2019, artikkel-id 1094920Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain's location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain's shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.

  • 18. Bernard, Olivier
    et al.
    Bosch, J G
    Heyde, Brecht
    Alessandrini, Martino
    Barbosa, Daniel
    Camarasu-Pop, S
    Cervenansky, F
    Valette, S
    Mirea, O
    Bernier, M
    Jodoin, P M
    Domingos, J S
    Stebbing, R V
    Keraudren, K
    Oktay, O
    Caballero, J
    Shi, W
    Rueckert, D
    Milletari, F
    Ahmadi, S A
    Smistad, E
    Lindseth, F
    van Stralen, M
    Wang, Chunliang
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Donal, E
    Monaghan, M
    Papachristidis, A
    Geleijnse, M L
    Galli, E
    Dhooge, Jan
    Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography.2015Inngår i: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254XArtikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from 3 experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

  • 19.
    Bernard, Olivier
    et al.
    University of Lyon 1, France.
    Bosch, Johan G.
    Erasmus MC, Netherlands.
    Heyde, Brecht
    Katholieke University of Leuven, Belgium.
    Alessandrini, Martino
    Katholieke University of Leuven, Belgium.
    Barbosa, Daniel
    University of Minho, Portugal.
    Camarasu-Pop, Sorina
    University of Lyon 1, France.
    Cervenansky, Frederic
    University of Lyon 1, France.
    Valette, Sebastien
    University of Lyon 1, France.
    Mirea, Oana
    Katholieke University of Leuven, Belgium.
    Bernier, Michel
    University of Sherbrooke, Canada.
    Jodoin, Pierre-Marc
    University of Sherbrooke, Canada.
    Santo Domingos, Jaime
    University of Oxford, England.
    Stebbing, Richard V.
    University of Oxford, England.
    Keraudren, Kevin
    University of London Imperial Coll Science Technology and Med, England.
    Oktay, Ozan
    University of London Imperial Coll Science Technology and Med, England.
    Caballero, Jose
    University of London Imperial Coll Science Technology and Med, England.
    Shi, Wei
    University of London Imperial Coll Science Technology and Med, England.
    Rueckert, Daniel
    University of London Imperial Coll Science Technology and Med, England.
    Milletari, Fausto
    Technical University of Munich, Germany.
    Ahmadi, Seyed-Ahmad
    University of Munich, Germany.
    Smistad, Erik
    Norwegian University of Science and Technology, Norway.
    Lindseth, Frank
    Norwegian University of Science and Technology, Norway.
    van Stralen, Maartje
    University of Medical Centre Utrecht, Netherlands.
    Wang, Chen
    Not Found:Linkoping Univ, Dept Med and Hlth Sci IMH, Ctr Med Imaging Sci and Visualizat CMIV, SE-58185 Linkoping, Sweden.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Donal, Erwan
    University of Rennes 1, France; University of Rennes 1, France; University of Rennes 1, France.
    Monaghan, Mark
    Kings Coll Hospital NHS Fdn Trust, England.
    Papachristidis, Alex
    Kings Coll Hospital NHS Fdn Trust, England.
    Geleijnse, Marcel L.
    Erasmus MC, Netherlands.
    Galli, Elena
    University of Rennes 1, France; University of Rennes 1, France; University of Rennes 1, France.
    Dhooge, Jan
    Katholieke University of Leuven, Belgium.
    Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography2016Inngår i: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 35, nr 4, s. 967-977Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts variability range. The platform remains open for new submissions.

  • 20. Blystad, I
    et al.
    Håkansson, I
    Tisell, A
    Ernerudh, J
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering. Linköping University.
    Lundberg, P
    Larsson, E-M
    Quantitative MRI for Analysis of Active Multiple Sclerosis Lesions without Gadolinium-Based Contrast Agent2015Inngår i: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959XArtikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND AND PURPOSE: Contrast-enhancing MS lesions are important markers of active inflammation in the diagnostic work-up of MS and in disease monitoring with MR imaging. Because intravenous contrast agents involve an expense and a potential risk of adverse events, it would be desirable to identify active lesions without using a contrast agent. The purpose of this study was to evaluate whether pre-contrast injection tissue-relaxation rates and proton density of MS lesions, by using a new quantitative MR imaging sequence, can identify active lesions.

    MATERIALS AND METHODS: Forty-four patients with a clinical suspicion of MS were studied. MR imaging with a standard clinical MS protocol and a quantitative MR imaging sequence was performed at inclusion (baseline) and after 1 year. ROIs were placed in MS lesions, classified as nonenhancing or enhancing. Longitudinal and transverse relaxation rates, as well as proton density were obtained from the quantitative MR imaging sequence. Statistical analyses of ROI values were performed by using a mixed linear model, logistic regression, and receiver operating characteristic analysis.

    RESULTS: Enhancing lesions had a significantly (P < .001) higher mean longitudinal relaxation rate (1.22 ± 0.36 versus 0.89 ± 0.24), a higher mean transverse relaxation rate (9.8 ± 2.6 versus 7.4 ± 1.9), and a lower mean proton density (77 ± 11.2 versus 90 ± 8.4) than nonenhancing lesions. An area under the receiver operating characteristic curve value of 0.832 was obtained.

    CONCLUSIONS: Contrast-enhancing MS lesions often have proton density and relaxation times that differ from those in nonenhancing lesions, with lower proton density and shorter relaxation times in enhancing lesions compared with nonenhancing lesions.

  • 21.
    Blystad, Ida
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Håkansson, I
    Tisell, Anders
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Ernerudh, J
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Larsson, EM
    Quantitative MRI for the evaluation of active MS-lesions without gadolinium based contrast agent.2014Konferansepaper (Annet vitenskapelig)
  • 22.
    Blystad, Ida
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper.
    Håkansson, Irene
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för neuro- och inflammationsvetenskap. Linköpings universitet, Medicinska fakulteten.
    Tisell, Anders
    Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper.
    Ernerudh, Jan
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för neuro- och inflammationsvetenskap. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Klinisk immunologi och transfusionsmedicin.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Larsson, Elna-Marie
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Uppsala University, Sweden.
    Quantitative MRI for Analysis of Active Multiple Sclerosis Lesions without Gadolinium-Based Contrast Agent2016Inngår i: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 37, nr 1, s. 94-100Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND AND PURPOSE: Contrast-enhancing MS lesions are important markers of active inflammation in the diagnostic work-up of MS and in disease monitoring with MR imaging. Because intravenous contrast agents involve an expense and a potential risk of adverse events, it would be desirable to identify active lesions without using a contrast agent. The purpose of this study was to evaluate whether pre-contrast injection tissue-relaxation rates and proton density of MS lesions, by using a new quantitative MR imaging sequence, can identify active lesions. MATERIALS AND METHODS: Forty-four patients with a clinical suspicion of MS were studied. MR imaging with a standard clinical MS protocol and a quantitative MR imaging sequence was performed at inclusion (baseline) and after 1 year. ROIs were placed in MS lesions, classified as nonenhancing or enhancing. Longitudinal and transverse relaxation rates, as well as proton density were obtained from the quantitative MR imaging sequence. Statistical analyses of ROI values were performed by using a mixed linear model, logistic regression, and receiver operating characteristic analysis. RESULTS: Enhancing lesions had a significantly (P &lt; .001) higher mean longitudinal relaxation rate (1.22 0.36 versus 0.89 +/- 0.24), a higher mean transverse relaxation rate (9.8 +/- 2.6 versus 7.4 +/- 1.9), and a lower mean proton density (77 +/- 11.2 versus 90 +/- 8.4) than nonenhancing lesions. An area under the receiver operating characteristic curve value of 0.832 was obtained. CONCLUSIONS: Contrast-enhancing MS lesions often have proton density and relaxation times that differ from those in nonenhancing lesions, with lower proton density and shorter relaxation times in enhancing lesions compared with nonenhancing lesions.

  • 23.
    Blystad, Ida
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Warntjes, Jan Bertus Marcel
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Klinisk fysiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Fysiologiska kliniken US.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Landtblom, Anne-Marie
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Neurologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Närsjukvården i centrala Östergötland, Neurologiska kliniken. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    SyntheticMRI compared with conventional MRI of the brain in a clinical setting: a pilot study, ESMRMB 2012, Lisbon, Portugal.2012Konferansepaper (Annet vitenskapelig)
  • 24.
    Blystad, Ida
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet.
    Warntjes, Jan Bertus Marcel
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Klinisk fysiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Fysiologiska kliniken US.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Landtblom, Anne-Marie
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Neurologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Närsjukvården i centrala Östergötland, Neurologiska kliniken.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Larsson, Elna-Marie
    Uppsala University, Sweden .
    Synthetic MRI of the brain in a clinical setting2012Inngår i: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, nr 10, s. 1158-1163Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND:

    Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on MR quantification, potentially both decreases examination time and enables quantitative measurements.

    PURPOSE:

    To evaluate synthetic MRI of the brain in a clinical setting by assessment of the contrast, the contrast-to-noise ratio (CNR), and the diagnostic quality compared with conventional MR images.

    MATERIAL AND METHODS:

    Twenty-two patients had synthetic imaging added to their clinical MR examination. In each patient, 12 regions of interest were placed in the brain images to measure contrast and CNR. Furthermore, general image quality, probable diagnosis, and lesion conspicuity were investigated.

    RESULTS:

    Synthetic T1-weighted turbo spin echo and T2-weighted turbo spin echo images had higher contrast but also a higher level of noise, resulting in a similar CNR compared with conventional images. Synthetic T2-weighted FLAIR images had lower contrast and a higher level of noise, which led to a lower CNR. Synthetic images were generally assessed to be of inferior image quality, but agreed with the clinical diagnosis to the same extent as the conventional images. Lesion conspicuity was higher in the synthetic T1-weighted images, which also had a better agreement with the clinical diagnoses than the conventional T1-weighted images.

    CONCLUSION:

    Synthetic MR can potentially shorten the MR examination time. Even though the image quality is perceived to be inferior, synthetic images agreed with the clinical diagnosis to the same extent as the conventional images in this study.

  • 25.
    Blystad, Ida
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Warntjes, Marcel Jan Bertus
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Fysiologiska kliniken US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. KTH Royal Institute Technology, Sweden.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Larsson, Elna-Marie
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Uppsala University, Sweden.
    Tisell, Anders
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Quantitative MRI for analysis of peritumoral edema in malignant gliomas2017Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, nr 5, artikkel-id e0177135Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background and purpose Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R-1, transverse relaxation R-2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images. Methods In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R-1, R-2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model. Results R-1, R-2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (Pamp;lt;.0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor. Conclusion Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R-1 and R-2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.

  • 26.
    Borgen, Lars
    et al.
    Drammen and Buskerud University of College.
    Kalra, Mannudeep K
    Harvard University.
    Laerum, Frode
    Akershus University Hospital.
    Hachette, Isabelle W
    ContextVision AB.
    Fredriksson, Carina H
    ContextVision AB, Linkoping, Sweden .
    Sandborg, Michael
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT2012Inngår i: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, nr 3, s. 335-342Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Abdominal computed tomography (an is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. less thanbrgreater than less thanbrgreater thanPurpose: To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. less thanbrgreater than less thanbrgreater thanMaterial and Methods: Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI (vol)) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. less thanbrgreater than less thanbrgreater thanResults: All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P andlt; 0.01). Standard dose images had better image quality than reduced dose 3D filtered images (P andlt; 0.01), but similar image noise. For patients with body mass index (BMI) andlt; 30 kg/m(2) however, 3D filtered images were rated significantly better than normal dose images for two image criteria (P andlt; 0.05), while no significant difference was found for the remaining three image criteria (P andgt; 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. less thanbrgreater than less thanbrgreater thanConclusion: The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI andlt; 30 kg/m(2), 3D filtered images are comparable to standard dose images.

  • 27.
    Brismar, T
    et al.
    Department of Radiology, CLINTEC, Stockholm, Sweden.
    Dahlström, Nils
    Linköpings universitet, Institutionen för medicin och vård. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Department of Radiology, Hudiksvall Hospital, Sweden.
    Smedby, Örjan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Persson, Anders
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Albiin, N
    Department of Radiology, CLINTEC, Stockholm, Sweden.
    Liver vessel enhancement by Gd-BOPTA and Gd-EOB-DTPA- a comparison in healthy volunteers2006Inngår i: ISMRM 2006,2006, 2006Konferansepaper (Annet vitenskapelig)
  • 28.
    Brismar, Torkel
    et al.
    Karolinska Institutet, CLINTEC, Röntgenavdelningen, Karolinska Universitetssjukhuset Huddinge.
    Dahlström, Nils
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Edsborg, Nick
    Karolinska Institutet, CLINTEC, Röntgenavdelningen, Karolinska Universitetssjukhuset Huddinge.
    Persson, Anders
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Albiin, Nils
    Karolinska Institutet, CLINTEC, Röntgenavdelningen, Karolinska Universitetssjukhuset Huddinge.
    Liver Vessel Enhancement by Gd-BOPTA and Gc-EOB-DTPA – a Comparison in Healthy Volunteers.2009Inngår i: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 50, nr 7, s. 709-715Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: A thorough understanding of magnetic resonance (MR) contrast media dynamics makes it possible to choose the optimal contrast media for each investigation. Differences in visualizing hepatobiliary function between Gd-BOPTA and Gd-EOB-DTPA have previously been demonstrated, but less has been published regarding differences in liver vessel visualization.Purpose: To compare the liver vessel and liver parenchymal enhancement dynamics of Gd-BOPTA (MultiHance®) and Gd-EOB-DTPA (Primovist®). Material and Methods: The signal intensity of the liver parenchyma, the common hepatic artery, the middle hepatic vein, and a segmental branch of the right portal vein, was obtained in 10 healthy volunteers before contrast media administration, during arterial and portal venous phases, and 10, 20, 30, 40 and 130 minutes after intravenous contrast medium injection, but due to scanner limitations not during the hepatic venous phase. Results: Maximum enhancement of liver parenchyma was observed from the portal venous phase until 130 minutes after Gd-BOPTA administration and from 10 minutes to 40 minutes after Gd-EOB-DTPA. There was no difference in maximum enhancement of liver parenchyma between the two contrast media. When using Gd-BOPTA, the vascular contrast enhancement was still apparent 40 minutes after injection, but had vanished 10 minutes after Gd-EOB-DTPA injection. The maximum difference in signal intensity between the vessels and the liver parenchyma was significantly greater with Gd-BOPTA than with Gd-EOB-DTPA (p<0.0001). Conclusion: At the dosage used in this study Gd-BOPTA yields higher maximum enhancement of the hepatic artery, portal vein and middle hepatic vein during the arterial and the portal venous phase and during the delayed phases than Gd-EOB-DTPA does, whereas there is no difference in liver parenchymal enhancement between the two contrast agents.

  • 29.
    Brusini, Irene
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Carneiro, Miguel
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;Univ Porto, Dept Biol, Fac Ciencias, P-4169007 Porto, Portugal..
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Rubin, Carl-Johan
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden..
    Ring, Henrik
    Uppsala Univ, Dept Neurosci, S-75236 Uppsala, Sweden..
    Afonso, Sandra
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal..
    Blanco-Aguiar, Jose A.
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;CSIC, Inst Invest Recursos Cineget IREC, Ciudad Real 13005, Spain.;UCLM, CSIC, Ciudad Real 13005, Spain..
    Ferrand, Nuno
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;Univ Porto, Dept Biol, Fac Ciencias, P-4169007 Porto, Portugal.;Univ Johannesburg, Dept Zool, ZA-2006 Auckland Pk, South Africa..
    Rafati, Nima
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden..
    Villafuerte, Rafael
    CSIC, IESA, Cordoba 14004, Spain..
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Damberg, Peter
    Karolinska Univ Hosp, Karolinska Expt Res & Imaging Ctr, S-17176 Solna, Sweden..
    Hallbook, Finn
    Uppsala Univ, Dept Neurosci, S-75236 Uppsala, Sweden..
    Fredrikson, Mats
    Uppsala Univ, Dept Psychol, S-75236 Uppsala, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    Andersson, Leif
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden.;Texas A&M Univ, Coll Vet Med & Biomed Sci, Dept Vet Integrat Biosci, College Stn, TX 77843 USA.;Swedish Univ Agr Sci, Dept Anim Breeding & Genet, S-75007 Uppsala, Sweden..
    Changes in brain architecture are consistent with altered fear processing in domestic rabbits2018Inngår i: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 115, nr 28, s. 7380-7385Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The most characteristic feature of domestic animals is their change in behavior associated with selection for tameness. Here we show, using high-resolution brain magnetic resonance imaging in wild and domestic rabbits, that domestication reduced amygdala volume and enlarged medial prefrontal cortex volume, supporting that areas driving fear have lost volume while areas modulating negative affect have gained volume during domestication. In contrast to the localized gray matter alterations, white matter anisotropy was reduced in the corona radiata, corpus callosum, and the subcortical white matter. This suggests a compromised white matter structural integrity in projection and association fibers affecting both afferent and efferent neural flow, consistent with reduced neural processing. We propose that compared with their wild ancestors, domestic rabbits are less fearful and have an attenuated flight response because of these changes in brain architecture.

  • 30.
    Brusini, Irene
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Jörgens, Daniel
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Moreno, Rodrigo
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Dependency of neural tracts'€™ curvature estimations on tractography methods2017Konferansepaper (Fagfellevurdert)
  • 31.
    Brusini, Irene
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Jörgens, Daniel
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Moreno, Rodrigo
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Influence of Tractography Algorithms and Settings on Local Curvature Estimations2017Konferansepaper (Fagfellevurdert)
  • 32.
    Brusini, Irene
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Jörgens, Daniel
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Moreno, Rodrigo
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper aims at proposing a method to generate atlases of white matter fibers’ geometry that consider local orientation and curvature of fibers extracted from tractography data. Tractography was performed on diffusion magnetic resonance images from a set of healthy subjects and each tract was characterized voxel-wise by its curvature and Frenet–Serret frame, based on which similar tracts could be clustered separately for each voxel and each subject. Finally, the centroids of the clusters identified in all subjects were clustered to create the final atlas. The proposed clustering technique showed promising results in identifying voxel-wise distributions of curvature and orientation. Two tractography algorithms (one deterministic and one probabilistic) were tested for the present work, obtaining two different atlases. A high agreement between the two atlases was found in several brain regions. This suggests that more advanced tractography methods might only be required for some specific regions in the brain. In addition, the probabilistic approach resulted in the identification of a higher number of fiber orientations in various white matter areas, suggesting it to be more adequate for investigating complex fiber configurations in the proposed framework as compared to deterministic tractography.

  • 33.
    Buizza, Giulia
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem. Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy..
    Toma-Dasu, Iuliana
    Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, Med Radiat Phys, S-17176 Solna, Sweden..
    Lazzeroni, Marta
    Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, Med Radiat Phys, S-17176 Solna, Sweden..
    Paganelli, Chiara
    Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy..
    Riboldi, Marco
    Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy.;Ludwig Maximilians Univ Munchen, Fac Phys, Coloumbwall 1, D-5748 Garching, Germany..
    Chang, Yong Jun
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans2018Inngår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, s. 21-29Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.

  • 34. Buizza, Giulia
    et al.
    Toma-Dasu, Iuliana
    Karolinska Institutet, Sweden.
    Lazzeroni, Marta
    Karolinska Institutet, Sweden.
    Paganelli, Chiara
    Riboldi, Marco
    Chang, Yongjun
    Smedby, Örjan
    Wang, Chunliang
    Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans2018Inngår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, s. 21-29Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose

    A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested.

    Methods

    The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters.

    Results

    The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters.

    Conclusions

    A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.

  • 35.
    Chang, Yongjun
    et al.
    KTH, Skolan för teknik och hälsa (STH).
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Effects of preprocessing in slice-level classification of interstitial lung disease based on deep convolutional networks2018Inngår i: VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017, Springer Netherlands, 2018, Vol. 27, s. 624-629Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Several preprocessing methods are applied to the automatic classification of interstitial lung disease (ILD). The proposed methods are used for the inputs to an established convolutional neural network in order to investigate the effect of those preprocessing techniques to slice-level classification accuracy. Experimental results demonstrate that the proposed preprocessing methods and a deep learning approach outperformed the case of the original images input to deep learning without preprocessing.

  • 36.
    Chowdhury, Manish
    et al.
    KTH, Skolan för teknik och hälsa (STH).
    Jörgens, Daniel
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Wang, Chunliang
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering. KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildteknik.
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Moreno, Rodrigo
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Segmentation of Cortical Bone using Fast Level Sets2017Inngår i: MEDICAL IMAGING 2017: IMAGE PROCESSING / [ed] Styner, MA Angelini, ED, SPIE - International Society for Optical Engineering, 2017, artikkel-id UNSP 1013327Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.

  • 37.
    Chowdhury, Manish
    et al.
    KTH, Skolan för teknik och hälsa (STH).
    Klintström, Benjamin
    KTH, Skolan för teknik och hälsa (STH). Linköping University, Sweden.
    Klintström, E.
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering. Linköping University, Sweden.
    Moreno, Rodrigo
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Granulometry-based trabecular bone segmentation2017Inngår i: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, s. 100-108Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The accuracy of the analyses for studying the three dimensional trabecular bone microstructure rely on the quality of the segmentation between trabecular bone and bone marrow. Such segmentation is challenging for images from computed tomography modalities that can be used in vivo due to their low contrast and resolution. For this purpose, we propose in this paper a granulometry-based segmentation method. In a first step, the trabecular thickness is estimated by using the granulometry in gray scale, which is generated by applying the opening morphological operation with ball-shaped structuring elements of different diameters. This process mimics the traditional sphere-fitting method used for estimating trabecular thickness in segmented images. The residual obtained after computing the granulometry is compared to the original gray scale value in order to obtain a measurement of how likely a voxel belongs to trabecular bone. A threshold is applied to obtain the final segmentation. Six histomorphometric parameters were computed on 14 segmented bone specimens imaged with cone-beam computed tomography (CBCT), considering micro-computed tomography (micro-CT) as the ground truth. Otsu’s thresholding and Automated Region Growing (ARG) segmentation methods were used for comparison. For three parameters (Tb.N, Tb.Th and BV/TV), the proposed segmentation algorithm yielded the highest correlations with micro-CT, while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performance was comparable to ARG. The method also yielded the strongest average correlation (0.89). When Tb.Th was computed directly from the gray scale images, the correlation was superior to the binary-based methods. The results suggest that the proposed algorithm can be used for studying trabecular bone in vivo through CBCT.

  • 38.
    Chowdhury, Manish
    et al.
    KTH, School of Technology and Health, Sweden.
    Klintström, Benjamin
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. KTH, School of Technology and Health, Sweden.
    Klintström, Eva
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Röntgenkliniken i Linköping. KTH, School of Technology and Health, Sweden.
    Moreno, Rodrigo
    KTH, School of Technology and Health, Sweden.
    Granulometry-Based Trabecular Bone Segmentation2017Inngår i: Image Analysis - 20th Scandinavian Conference on Image Analysis, SCIA 2017, Proceedings / [ed] Sharma P., Bianchi F., Springer, 2017, Vol. 10270, s. 100-108Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The accuracy of the analyses for studying the three dimensionaltrabecular bone microstructure rely on the quality of the segmentationbetween trabecular bone and bone marrow. Such segmentationis challenging for images from computed tomography modalities thatcan be used in vivo due to their low contrast and resolution. For thispurpose, we propose in this paper a granulometry-based segmentationmethod. In a first step, the trabecular thickness is estimated by usingthe granulometry in gray scale, which is generated by applying the openingmorphological operation with ball-shaped structuring elements ofdifferent diameters. This process mimics the traditional sphere-fittingmethod used for estimating trabecular thickness in segmented images.The residual obtained after computing the granulometry is comparedto the original gray scale value in order to obtain a measurement ofhow likely a voxel belongs to trabecular bone. A threshold is applied toobtain the final segmentation. Six histomorphometric parameters werecomputed on 14 segmented bone specimens imaged with cone-beam computedtomography (CBCT), considering micro-computed tomography(micro-CT) as the ground truth. Otsu’s thresholding and AutomatedRegion Growing (ARG) segmentation methods were used for comparison.For three parameters (Tb.N, Tb.Th and BV/TV), the proposedsegmentation algorithm yielded the highest correlations with micro-CT,while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performancewas comparable to ARG. The method also yielded the strongestaverage correlation (0.89). When Tb.Th was computed directly fromthe gray scale images, the correlation was superior to the binary-basedmethods. The results suggest that the proposed algorithm can be usedfor studying trabecular bone in vivo through CBCT.

  • 39.
    Chowdhury, Manish
    et al.
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Rota Bulò, S.
    Moreno, Rodrigo
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Kundu, M.K.
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network2016Inngår i: 2016 23rd International Conference on Pattern Recognition (ICPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 3134-3139, artikkel-id 7900116Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high- level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

  • 40.
    Cros, Olivier
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Department of Otolaryngology, Head and Neck Surgery, Aalborg Hospital, Aarhus University Hospital, Denmark.
    Gaihede, Michael L.
    Department of Otolaryngology, Head and Neck Surgery, Aalborg Hospital, Aarhus University Hospital, Denmark.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Mastoid structural properties determined by imaging analysis of high resolution CT-scanning2010Inngår i: Hearing Research, ISSN 0378-5955, E-ISSN 1878-5891, Vol. 263, nr 1-2, s. 242-243Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Hypothesis: The structure of the mastoid air cells can be described by quantitative imaging analysis of high-resolution CT-scans, which may contribute to understand its function in normal and pathological ears. Background: Negative middle ear pressure is a common factor in middle ear diseases resulting from an imbalance between mastoid gas exchange and Eustachian tube function. While the Eustachian tube function has been the main focus of research, more recent studies indicate that the mastoid may play an active role in pressure regulation. The mastoid structure with numerous air cells reflects a large area to volume ratio (AV-ratio) adapted to efficient gas exchange. Imaging analysis applied to high resolution CT-scanning can describe quantitative measures, which may reveal important information about mastoid function and its role in healthy and diseased ears. Materials and methods: Quantitative analysis was performed on a series of unselected high resolution CT-scans (voxel size: 0.29 _ 0.29 _ 0.625 mm) from 36 ears in 24 patients. Area and volume were determined using Cavalieri’s method, i.e. by summing cross-sectional areas. The AV-ratio was computed for each scan. Results: Mean area was 69 cm2 (range: 23–134cm2), mean volume was 4 cm3 (range: 1.3–10.8 cm3), and mean AV-ratio was 16 cm-1 (range: 11.2–21.0 cm-1). The area correlated linearly to the volume by A = 17.2*V-0.2. Conclusion: The area and volume values corresponded with previous studies, and the additional AV-ratio reflected the functional properties of the mastoid in terms of capability for gas exchange. Due to a series of similarities between structure and function of the lungs and mastoid, it seems likely to propose a tree-structure of dividing mastoid cells. In respiratory research, analysis describing the dimensions of series of bronchi generations has been applied, and based on current results; our aim of future research is to establish similar details of mastoid tree-structure. Funding source: Various private Danish funds.

  • 41.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och hälsa. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Dahlström, Nils
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Brismar, T
    Sandström, Per
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Kirurgi. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Kirurgiska kliniken i Östergötland med verksamhet i Linköping, Norrköping och Motala.
    Kihlberg, Johan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och hälsa. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    A liver function test based on measurement of liver-specific contrast agent uptake2008Inngår i: Proceedings 16th Scientific meeting, ISMRM,2008, 2008Konferansepaper (Annet vitenskapelig)
    Abstract [en]

      

  • 42.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Dahlström, Nils
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Kihlberg, Johan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Sandström, Per
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Kirurgi.
    Brismar, Torkel
    Department of Clinical Science, Intervention and Technology at Karolinska Institutet, Division of Medical Imaging and Technology, Karolinska University Hospital in Huddinge, Stockholm, Sweden.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Quantifying differences in hepatic uptake of the liver specific contrast agents Gd-EOB-DTPA and Gd-BOPTA: a pilot study2012Inngår i: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 22, nr 3, s. 642-653Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives   To develop and evaluate a procedure for quantifying the hepatocyte-specific uptake of Gd-BOPTA and Gd-EOB-DTPA using dynamic contrast-enhanced (DCE) MRI. Methods   Ten healthy volunteers were prospectively recruited and 21 patients with suspected hepatobiliary disease were retrospectively evaluated. All subjects were examined with DCE-MRI using 0.025 mmol/kg of Gd-EOB-DTPA. The healthy volunteers underwent an additional examination using 0.05 mmol/kg of Gd-BOPTA. The signal intensities (SI) of liver and spleen parenchyma were obtained from unenhanced and enhanced acquisitions. Using pharmacokinetic models of the liver and spleen, and an SI rescaling procedure, a hepatic uptake rate, K Hep, estimate was derived. The K Hep values for Gd-EOB-DTPA were then studied in relation to those for Gd-BOPTA and to a clinical classification of the patient’s hepatobiliary dysfunction. Results   K Hep estimated using Gd-EOB-DTPA showed a significant Pearson correlation with K Hep estimated using Gd-BOPTA (r = 0.64; P < 0.05) in healthy subjects. Patients with impaired hepatobiliary function had significantly lower K Hep than patients with normal hepatobiliary function (K Hep = 0.09 ± 0.05 min-1 versus K Hep = 0.24 ± 0.10 min−1; P < 0.01). Conclusions   A new procedure for quantifying the hepatocyte-specific uptake of T 1-enhancing contrast agent was demonstrated and used to show that impaired hepatobiliary function severely influences the hepatic uptake of Gd-EOB-DTPA. Key Points   • The liver uptake of contrast agents may be measured with standard clinical MRI.Calculation of liver contrast agent uptake is improved by considering splenic uptake.Liver function affects the uptake of the liver-specific contrast agent Gd-EOB-DTPA.Hepatic uptake of two contrast agents (Gd-EOB-DTPA, Gd-BOPTA) is correlated in healthy individuals.This method can be useful for determining liver function, e.g. before hepatic surgery

  • 43.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Dahlström, Nils
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Sandström, P
    Brismar, Torkel
    Karolinska institutet.
    Kihlberg, Johan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Hälsouniversitetet.
    A liver function test based on measurement of liver specific contrast agent uptake2008Konferansepaper (Annet vitenskapelig)
  • 44.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Dahlström, Nils
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Sandström, P
    Freij, Anna
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Kihlberg, Johan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Brismar, Torkel
    Karolinska institutet.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    The hepatic uptake of Gd-EOB-DTPA is strongly affected by the hepatobiliary function2009Konferansepaper (Annet vitenskapelig)
  • 45.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Dahlström, Nils
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Sandström, P
    Kihlberg, Johan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Brismar, Torkel
    Karolinska institutet.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping. Linköpings universitet, Hälsouniversitetet.
    The hepatic uptake of Gd-EOB-DTPA is strongly correlated with the uptake of Gd-BOPTA2010Konferansepaper (Annet vitenskapelig)
  • 46.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Jacek, J.
    Aalto, Anne
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum.
    Grönqvist, A.
    Smedby, Örjan
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum.
    Landtblom, Anne-Marie
    Linköpings universitet, Institutionen för klinisk och experimentell medicin. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Närsjukvården i centrala Östergötland, Neurologiska kliniken.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen.
    Is Increased Normal White Matter Glutamate Concentration a Precursor of Gliosis and Disease Progression in Multiple Sclerosis?Manuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Background: The multiple sclerosis (MS) severity scale (MSSS) is a new scoring procedure to clinically characterize the rate of disease progression in MS, rather than the disability of the patient. The latter is often characterized using the expanded disability status score (EDSS). The progress rate of the disease, magnetic resonance imaging (MRI)-based measures of ‘black hole lesions’, and atrophy have all been shown to be predicted well by MSSS. In this study we investigated possible relationships between brain metabolite concentrations, measured using proton (1H) magnetic resonance spectroscopy (MRS), and MSSS.

    Purpose: Our aims were to quantitatively investigate the metabolite concentrations in normal appearing white matter (NAWM) in MS-patients, and also to investigate possible correlations between disease subtype, EDSS and MSSS and metabolite concentrations. To minimize the interference from lesion contamination in the MRS measurement, a refined novel analysis procedure had to be developed in order to correct for partial volume effects in tissues near plaques.

    Materials and Methods: Forty eight patients with Clinically Definite MS (CDMS), and 18 normal control subjects (NC) were included retrospectively from several MRS studies. T1, T2, and proton density MRI, and four white matter 1H MRS single voxel PRESS (Point-REsolved SpectroScopy) spectra were acquired in each subject using echo time 35 ms and repetition time 6000 ms on a 1.5 T MR-scanner. A total of 108 examinations were acquired from patients and 18 from NC. Absolutely quantified NAWM metabolite concentrations were determined using a mixed linear model (MLM) analysis that included the degree of T2 lesion contamination in each voxel. The T2 lesion contamination of the MRS voxels was also used as an estimate of ‘lesion load’ at each exam. The corrected metabolite concentrations were then correlated with clinical measures of the patients’ status, including EDSS and MSSS.

    Results: The axonal marker N-acetyl aspartate (NAA) did not correlate with either EDSS or MSSS. The glial cell markers creatine and myo-inositol correlated positively with EDSS. Creatine and glutamate correlated positively with MSSS. The ‘estimated lesion load’ correlated positively not only with EDSS, but also with the number of bouts since disease onset. Importantly, it did not correlate with MSSS.

    Conclusion: The most interesting findings were the unchanged concentrations of NAA, and the concomitant increase of creatine and myo-inositol during the course of disease progression in MSpatients. These not only indicated a constant axonal density, but also that a simultaneous development of gliosis occurred. These processes are most likely linked to demyelination, as well as development of white matter atrophy, a process in which the demyelinated volume is replaced by the surrounding tissue leading to a net loss of white matter. As a consequence of this process, axons in NAWM are probably damaged, which leads to a higher concentration of glia cells relative to the axonal volume. The positive correlation that was found between MSSS, and the glutamate and creatine concentrations in NAWM, in combination with a complete lack of correlation between lesion load and MSSS, suggests that altered glutamate metabolism, and subsequent demyelination and gliosis, is an important pathophysiological mechanism in MS.

  • 47.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Jaworski, J,
    Östergötlands Läns Landsting, Närsjukvården i centrala Östergötland, Neurologiska kliniken.
    Aalto, Anne
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Grönkvist, Anders
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa. Linköpings universitet, Hälsouniversitetet.
    Tisell, Anders
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Linköpings universitet, Hälsouniversitetet.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Landtblom, Anne-Marie
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Neurologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Närsjukvården i centrala Östergötland, Neurologiska kliniken. Östergötlands Läns Landsting, Närsjukvården i västra Östergötland, Medicinska specialistkliniken .
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Is Increased normal White Matter Glutamate Concentrations a Precursor of Gliosis and Disease Progression in Multiple Sclerosis?2011Inngår i: Internationell Society for Magnetic Resonance in Medicin, 2011, 2011, s. 4089-4089Konferansepaper (Fagfellevurdert)
  • 48.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa. Linköpings universitet, Hälsouniversitetet.
    Johansson, Andreas
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Rydell, Joakim
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Kihlberg, Johan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och hälsa. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Nyström, Fredrik H.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa. Linköpings universitet, Hälsouniversitetet.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Bildmedicinskt centrum, Röntgenkliniken i Linköping.
    Borga, Magnus
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Quantification of abdominal fat accumulation during hyperalimentation using MRI2009Inngår i: Proceedings of the ISMRM Annual Meeting (ISMRM'09), 2009, Berkeley, CA, USA: International Society for Magnetic Resonance in Medicine , 2009, s. 206-Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    There is an increasing demand for imaging methods that can be used for automatic, accurate and quantitative determination of the amounts of abdominal fat. Such methods are important as they will allow the evaluation of some of the risk factors underlying the ’metabolic syndrome’. The metabolic syndrome is becoming common in large parts of the world, and it appears that a dominant risk factor for developing this syndrome is abdominal obesity. Subjects that are afflicted with the metabolic syndrome are exposed to a high risk for developing a large range of diseases such as type 2 diabetes, cardiac failure, and stroke. The aim of this work

  • 49.
    Dahlqvist Leinhard, Olof
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Romu, Thobias
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Kihlberg, Johan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet.
    Gjellan, Solveig
    Linköpings universitet, Institutionen för medicin och hälsa, Internmedicin. Linköpings universitet, Hälsouniversitetet.
    Zanjani, Sepehr
    Linköpings universitet, Institutionen för medicin och hälsa, Internmedicin. Linköpings universitet, Hälsouniversitetet.
    Smedby, Örjan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Diagnostikcentrum, Röntgenkliniken i Linköping.
    Nyström, Fredrik
    Linköpings universitet, Institutionen för medicin och hälsa, Internmedicin. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Endokrinmedicinska enheten.
    Borga, Magnus
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Validation of whole-­‐body adipose tissue quantification using air displacement plethysmometry2012Inngår i: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Konferansepaper (Annet vitenskapelig)
  • 50.
    Dahlström, N
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Brismar, TB
    Persson, Anders
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Smedby, Örjan
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiologi. Östergötlands Läns Landsting, Bildmedicinskt centrum, Avdelningen för radiologi US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Albiin, N
    Biliary enhancement of Gd-BOPTA and Gd-EOB-DTPA - a study in healthy volunteers2006Inngår i: ISMRM,2006, 2006Konferansepaper (Annet vitenskapelig)
1234567 1 - 50 of 304
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