Change search
Refine search result
1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Asklund, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Hauksson, Jon
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Henriksson, Roger
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Evaluation of advanced MR techniques for development of early biomarkers for treatment efficacy in malignant brain tumors2010Conference paper (Refereed)
  • 2.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Applications of statistical methods in quantitative magnetic resonance imaging2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).

    DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.

    DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.

    However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.

    In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.

  • 3.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Holmberg, August
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Goldhaber, David
    Jian, Yiqiang
    Illerstam, Fredrik
    Engström, Mathias
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Technical note: adapting a GE SIGNA PET/MR scanner for radiotherapy2018In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 8, p. 3546-3550Article in journal (Refereed)
    Abstract [en]

    Purpose: Simultaneous collection of PET and MR data for radiotherapy purposes are useful for, for example, target definition and dose escalations. However, a prerequisite for using PET/MR in the radiotherapy workflow is the ability to image the patient in treatment position. The aim of this work was to adapt a GE SIGNA PET/MR scanner to image patients for radiotherapy treatment planning and evaluate the impact on signal-to-noise (SNR) of the MR images, and the accuracy of the PET attenuation correction. Method: A flat tabletop and a coil holder were developed to image patients in the treatment position, avoid patient contour deformation, and facilitate attenuation correction of flex coils. Attenuation corrections for the developed hardware and an anterior array flex coil were also measured and implemented to the PET/MR system to minimize PET quantitation errors. The reduction of SNR in the MR images due to the added distance between the coils and the patient was evaluated using a large homogenous saline-doped water phantom, and the activity quantitation errors in PET imaging were evaluated with and without the developed attenuation corrections. Result: We showed that the activity quantitation errors in PET imaging were within ±5% when correcting for attenuation of the flat tabletop, coil holder, and flex coil. The SNR of the MRI images were reduced to 74% using the tabletop, and 66% using the tabletop and coil holders. Conclusion: We present a tabletop and coil holder for an anterior array coil to be used with a GE SIGNA PET/MR scanner, for scanning patients in the radiotherapy work flow. Implementing attenuation correction of the added hardware from the radiotherapy setup leads to acceptable PET image quantitation. The drop in SNR in MR images may require adjustment of the imaging protocols.

  • 4.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Gray-level invariant Haralick texture features2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S279-S280Article in journal (Other academic)
  • 5.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nilsson, David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Henriksson, Roger
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Hauksson, Jon
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Birgander, Richard
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    ADC texture-An imaging biomarker for high-grade glioma?2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 10, p. 101903-Article in journal (Refereed)
    Abstract [en]

    Purpose:

    Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers.

    Methods:

    Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression.

    Results:

    The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001.

    Conclusions:

    By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort. (C) 2014 Author(s).

  • 6.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nilsson, David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Torheim, Turid
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 4041Article in journal (Refereed)
    Abstract [en]

    In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

  • 7.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Wirestam, Ronnie
    Lund University.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. CJ Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands.
    Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling2015In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 74, no 4, p. 1156-1164Article in journal (Refereed)
    Abstract [en]

    Purpose: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI.

    Methods: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T.

    Results: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter.

    Conclusion: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo.

  • 8.
    Bylund, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Lundman, Josef
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Using deep learning to generate synthetic CTs for radiotherapy treatment planning2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S283-S283Article in journal (Other academic)
  • 9.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Kuess, Peter
    Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Georg, Dietmar
    Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Helbich, Thomas H.
    Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis2018In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 63, no 19, p. 9-15, article id 195017Article in journal (Refereed)
    Abstract [en]

    The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).

    The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features.

    Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes.

    The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about 20×20).

    In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.

  • 10.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Sveriges lantbruksuniversitet, Centre of Biostochastiscs.
    Wirestam, Ronnie
    Lunds universitet, Medicinsk strålningsfysik.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Uncertainty estimation in dynamic contrast-enhanced MRI2013In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 69, no 4, p. 992-1002Article in journal (Refereed)
    Abstract [en]

    Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for Ktrans, ve, and vp, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.

  • 11.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Wirestam, Ronnie
    Radiofysik, Lunds Universitet.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thomas, Asklund
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Uncertainty Maps in Dynamic Contrast-Enhanced MRI2012Conference paper (Refereed)
  • 12.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Wirestam, Ronnie
    Radiofysik, Lunds Universitet.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thomas, Asklund
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Uncertainty Maps in Dynamic Contrast-Enhanced MRI2012Conference paper (Refereed)
  • 13.
    Jonsson, Joakim H
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Registration accuracy for MR images of the prostate using a subvolume based registration protocol2011In: Radiation Oncology, ISSN 1748-717X, E-ISSN 1748-717X, Vol. 6, no 1, p. 73-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate.

    METHODS: Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances.

    RESULTS: We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series.

    CONCLUSIONS: Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.

  • 14.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Gray-level invariant Haralick texture features2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 2, article id e0212110Article in journal (Refereed)
    Abstract [en]

    Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.

  • 15.
    Rutegård, Miriam
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Båtsman, Malin
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Brännström, Fredrik
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Rutegård, Jörgen
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Ljuslinder, Ingrid
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Palmqvist, Richard
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Rutegård, Martin
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    PET/MRI and PET/CT hybrid imaging of rectal cancer - description and initial observations from the RECTOPET (REctal Cancer trial on PET/MRI/CT) study2019In: Cancer Imaging, ISSN 1740-5025, E-ISSN 1470-7330, Vol. 19, article id 52Article in journal (Refereed)
    Abstract [en]

    PurposeThe role of hybrid imaging using F-18-fluoro-2-deoxy-D-glucose positron-emission tomography (FDG-PET), computed tomography (CT) and magnetic resonance imaging (MRI) to improve preoperative evaluation of rectal cancer is largely unknown. To investigate this, the RECTOPET (REctal Cancer Trial on PET/MRI/CT) study has been launched with the aim to assess staging and restaging of primary rectal cancer. This report presents the study workflow and the initial experiences of the impact of PET/CT on staging and management of the first patients included in the RECTOPET study.MethodsThis prospective cohort study, initiated in September 2016, is actively recruiting patients from Region Vasterbotten in Sweden. This pilot study includes patients recruited and followed up until December 2017. All patients had a biopsy-verified rectal adenocarcinoma and underwent a minimum of one preoperative FDG-PET/CT and FDG-PET/MRI examination. These patients were referred to the colorectal cancer multidisciplinary team meeting at Umea University Hospital. All available data were evaluated when making management recommendations. The clinical course was noted and changes consequent to PET imaging were described; surgical specimens underwent dedicated MRI for anatomical matching between imaging and histopathology.ResultsTwenty-four patients have so far been included in the study. Four patients were deemed unresectable, while 19 patients underwent or were scheduled for surgery; one patient was enrolled in a watch-and-wait programme after restaging. Consequent to taking part in the study, two patients were upstaged to M1 disease: one patient was diagnosed with a solitary hepatic metastasis detected using PET/CT and underwent metastasectomy prior to rectal cancer surgery, while one patient with a small, but metabolically active, lung nodulus experienced no change of management. PET/MRI did not contribute to any recorded change in patient management.ConclusionsThe RECTOPET study investigating the role of PET/CT and PET/MRI for preoperative staging of primary rectal cancer patients will provide novel data that clarify the value of adding hybrid to conventional imaging, and the role of PET/CT versus PET/MRI.Trial registrationNCT03846882.

  • 16. Skorpil, M.
    et al.
    Ryden, H.
    Berglund, J.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brosjö, O.
    Tsagozis, R.
    Soft-tissue fat tumours: differentiating malignant from benign using proton density fat fraction quantification MRI2019In: Clinical Radiology, ISSN 0009-9260, E-ISSN 1365-229X, Vol. 74, no 7, p. 534-538Article in journal (Refereed)
    Abstract [en]

    Aim: To evaluate if quantifying proton density fat fraction (PDFF) would be useful in separating lipoma, atypical lipomatous tumour (ALT) and liposarcoma in the extremities and trunk. In addition, differentiating ALT versus non-classical lipomas using magnetic resonance imaging (MRI)-based fatty acidcomposition (FAC) and three-dimensional (3D) texture analysis was tested.

    Material and methods: This prospective study (undertaken between 2014–2017; comprising 20 women, 21 men) was approved by the Regional Ethical Review Board and informed consent was obtained from all participants. For PDFF and FAC 3D spoiled gradient multi-echo images were acquired. PDFF was analysed in 16 lipomas (25–76 years), 14 ALTs (42–78 years) and 11 myxoid liposarcomas (31–68 years). The difference of mean PDFF was tested with one-way analysis of variance. A support vector machine algorithm was used to find the separating mean PDFF values.

    Results: Mean PDFF for lipomas was 90% (range 76–98%), for ALT 83% (range 62–91%), and for liposarcoma 4% (range 0–21%). The difference of mean PDFF for liposarcomas versus ALT and lipoma was significant (p=0.0001, for both), and for ALT versus lipoma (p=0.021). The optimal threshold for separating liposarcoma from ALT and lipoma was 41.5%, and for ALT and lipoma 85%. Texture analysis could not separate ALT and non-classical lipomas, while the difference for FAC unsaturation degree was significant (p=0.013).

    Conclusion: Measuring PDFF is a promising complement to standard MRI, to separate liposarcomas from ALT and lipomas. Lipomas that are not solely composed of fat cannot confidently be separated from ALT using PDFF, FAC, or texture analysis.

  • 17.
    Skorpil, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Department of Radiology, Uppsala University Hospital, Uppsala, Sweden.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Engström, M.
    Motion corrected DWI with integrated T2-mapping for simultaneous estimation of ADC, T2-relaxation and perfusion in prostate cancer2017In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 39, p. 162-167Article in journal (Refereed)
    Abstract [en]

    Objective: Multiparametric magnetic resonance imaging (MRI) and PI-RADS (Prostate Imaging - Reporting and Data System) has become the standard to determine a probability score for a lesion being a clinically significant prostate cancer. T2-weighted and diffusion-weighted imaging (DWI) are essential in PI-RADS, depending partly on visual assessment of signal intensity, while dynamic-contrast enhanced imaging is less important. To decrease inter-rater variability and further standardize image evaluation, complementary objective measures are in need. Methods: We here demonstrate a sequence enabling simultaneous quantification of apparent diffusion coefficient (ADC) and T2-relaxation, as well as calculation of the perfusion fraction f from low b-value intravoxel incoherent motion data. Expandable wait pulses were added to a FOCUS DW SE-EPI sequence, allowing the effective echo time to change at run time. To calculate both ADC and f, b-values 200 s/mm(2) and 600 s/mm(2) were chosen, and for T2-estimation 6 echo times between 64.9 ms and 114.9 ms were used. Results: Three patients with prostate cancer were examined and all had significantly decreased ADC and T2 values, while f was significantly increased in 2 of 3 tumors. T2 maps obtained in phantom measurements and in a healthy volunteer were compared to T2 maps from a SE sequence with consecutive scans, showing good agreement. In addition, a motion correction procedure was implemented to reduce the effects of prostate motion, which improved T2-estimation. Conclusions: This sequence could potentially enable more objective tumor grading, and decrease the inter-rater variability in the PI-RADS classification.

  • 18.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Liu, Xijia
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Contrast agent quantification by using spatial information in dynamic contrast enhanced MRI2016Manuscript (preprint) (Other academic)
    Abstract [en]

    The purpose of this study is to investigate a method, using simulations, toimprove contrast agent quantication in Dynamic Contrast Enhanced MRI.Bayesian hierarchical models (BHMs) are applied to smaller images such that spatial information can be incorporated. Then exploratory analysisis done for larger images by using maximum a posteriori (MAP).

    For smaller images: the estimators of proposed BHMs show improvementsin terms of the root mean squared error compared to the estimators in existingmethod for a noise level equivalent of a 12-channel head coil at 3T. Moreover,Leroux model outperforms Besag models. For larger images: MAP estimatorsalso show improvements by assigning Leroux prior.

  • 19.
    wang, jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Liu, Xijia
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRIManuscript (preprint) (Other academic)
  • 20.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using Bayesian hierarchical model2016In: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling, 2016, p. 217-217Conference paper (Other academic)
1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf