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  • 1.
    Bayisa, Fekadu
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Kuljus, Kristi
    Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Bolin, David
    Department of Mathematical Sciences, Chalmers and University of Gothenburg, Gothenburg, Sweden.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Prediction of CT images from MR images with hidden Markov and random field models2016In: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling / [ed] A. Iftimi, J. Mateu and F. Montes, 2016, p. 163-163Conference paper (Other academic)
  • 2.
    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.

  • 3.
    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)
  • 4.
    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)
  • 5.
    Hildeman, Anders
    et al.
    Department of Mathematical Sciences, Chalmers University of Technology, Sweden.
    Bolin, David
    Department of Mathematical Sciences, Chalmers University of Technology, Sweden.
    Wallin, Jonas
    Department of Statistics, Lund University.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Hildeman, A., Bolin, D., Wallin, J., Johansson, A., Nyholm, T., Asklund, T., and Yu, J. Whole-brain substitute CT generation using Markov random field mixture models.2016Manuscript (preprint) (Other academic)
    Abstract [en]

    Computed tomography (CT) equivalent information is needed for attenuation correction in PET imaging and for dose planning in radiotherapy. Prior work has shown that Gaussian mixture models can be used to generate a substitute CT (s-CT) image from a specific set of MRI modalities. This work introduces a more flexible class of mixture models for s-CT generation, that incorporates spatial dependency in the data through a Markov random field prior on the latent field of class memberships associated with a mixture model. Furthermore, the mixture distributions are extended from Gaussian to normal inverse Gaussian (NIG), allowing heavier tails and skewness. The amount of data needed to train a model for s-CT generation is of the order of 10^8 voxels. The computational efficiency of the parameter estimationand prediction methods are hence paramount, especially when spatial dependency is included in the models. A stochastic Expectation Maximization (EM) gradient algorithm is proposed in order to tackle this challenge. The advantages of the spatial model and NIG distributions are evaluated with a cross-validation study based ondata from 14 patients. The study show that the proposed model enhances the predictive quality of the s-CT images by reducing the mean absolute error with 17.9%. Also, the distribution of CT values conditioned on the MR images are better explainedby the proposed model as evaluated using continuous ranked probability scores.

  • 6.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Magnetic resonance imaging with ultrashort echo time as a substitute for X-ray computed tomography2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Radiotherapy dose calculations have evolved from simple factor based methods performed with pen and paper, into computationally intensive simulations based on Monte Carlo theory and energy deposition kernel convolution.

    Similarly, in the field of positron emission tomography (PET), attenuation correction, which was originally omitted entirely, is now a crucial component of any PET reconstruction algorithm.

    Today, both of these applications – radiotherapy and PET – derive their needed in-tissue radiation attenuation coefficients from images acquired with X-ray computed tomography (CT). Since X-ray images are themselves acquired using ionizing radiation, the intensity at a point in an image will reflect the radiation interaction properties of the tissue located at that point.

    Magnetic resonance imaging (MRI), on the other hand, does not use ionizing radiation. Instead MRI make use of the net transverse magnetization resulting from the spin polarization of hydrogen nuclei. MR image contrast can be varied to a greater extent than CT and the soft tissue contrast is, for most MR sequences, superior to that of CT. Therefore, for many cases, MR images provide a considerable advantage over CT when identifying or delineating tumors or other diseased tissues.

    For this reason, there is an interest to replace CT with MRI for a great number of diagnostic and therapeutic workflows. Also, replacing CT with MRI would reduce the exposure to ionizing radiation experienced by patients and, by extension, reduce the associated risk to induce cancer.

    In part MRI has already replaced CT, but for radiotherapy dose calculations and PET attenuation correction, CT examinations are still necessary in clinical practice. One of the reasons is that the net transverse magnetization imaged in MRI cannot be converted into attenuation coefficients for ionizing radiation in a straightforward way. More specifically, regions with similar appearance in magnetic resonance (MR) images, such as bone and air pockets, are found at different ends of the spectrum of attenuation coefficients present in the human body. In a CT image, bone will appear bright white and air as black corresponding to high and no attenuation, respectively. In an MR image, bone and air both appear dark due to the lack of net transverse magnetization.

    The weak net transverse magnetization of bone is a result of low hydrogen density and rapid transverse relaxation. A particular category of MRI sequences with so-called ultrashort echo time (UTE) can sample the MRI signal from bone before it is lost due to transverse relaxation. Thus, UTE sequences permit bone to be imaged with MRI albeit with weak intensity and poor resolution.

    Imaging with UTE in combination with careful image analysis can permit ionizing-radiation attenuation-maps to be derived from MR images. This dissertation and appended articles present a procedure for this very purpose. However, as attenuation coefficients are radiation-quality dependent the output of the method is a Hounsfield unit map, i.e. a substitute for a CT image. It can be converted into an attenuation map using conventional clinical procedure.

    Obviating the use of CT would reduce the number of examinations that patients have to endure during preparation for radiotherapy. It would also permit PET attenuation correction to be performed on images from the new imaging modality that combines PET and MRI in one scanner – PET/MR.

  • 7.
    Johansson, Adam
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Tufve, Nyholm
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    CT substitutes derived from MR images reconstructed with parallel imaging2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 8, p. 474-480Article in journal (Refereed)
    Abstract [en]

    Purpose: Computed tomography (CT) substitute images can be generated from ultrashort echo time (UTE) MRI sequences with radial k-space sampling. These CT substitutes can be used as ordinary CT images for PET attenuation correction and radiotherapy dose calculations. Parallel imaging allows faster acquisition of magnetic resonance (MR) images by exploiting differences in receiver coil element sensitivities. This study investigates whether non-Cartesian parallel imaging reconstruction can be used to improve CT substitutes generated from shorter examination times.

    Methods: The authors used gridding as well as two non-Cartesian parallel imaging reconstruction methods, SPIRiT and CG-SENSE, to reconstruct radial UTE and gradient echo (GE) data into images of the head for 23 patients. For each patient, images were reconstructed from the full dataset and from a number of subsampled datasets. The subsampled datasets simulated shorter acquisition times by containing fewer radial k-space spokes (1000, 2000, 3000, 5000, and 10 000 spokes) than the full dataset (30 000 spokes). For each combination of patient, reconstruction method, and number of spokes, the reconstructed UTE and GE images were used to generate a CT substitute. Each CT substitute image was compared to a real CT image of the same patient.

    Results: The mean absolute deviation between the CT number in CT substitute and CT decreased when using SPIRiT as compared to gridding reconstruction. However, the reduction was small and the CT substitute algorithm was insensitive to moderate subsampling (≥5000 spokes) regardless of reconstruction method. For more severe subsampling (≤3000 spokes), corresponding to acquisition times less than aminute long, the CT substitute quality was deteriorated for all reconstructionmethods but SPIRiT gave a reduction in the mean absolute deviation of down to 25 Hounsfield units compared to gridding.

    Conclusions: SPIRiT marginally improved the CT substitute quality for a given number of radial spokes as compared to gridding. However, the increased reconstruction time of non-Cartesian parallel imaging reconstruction is difficult to motivate from this improvement. Because the CT substitute algorithm was insensitive to moderate subsampling, data for a CT substitute could be collected in as little as minute and reconstructed with gridding without deteriorating the CT substitute quality.

  • 8.
    Johansson, Adam
    et al.
    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.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information: potential application for MR-only radiotherapy and attenuation correction in positron emission tomography2013In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 52, no 7, p. 1369-1373Article in journal (Refereed)
    Abstract [en]

    Background: Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT. Material and methods: MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient. Results: The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities. Conclusions: This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.

  • 9.
    Johansson, Adam
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    CT substitute derived from MRI sequences with ultrashort echo time2011In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 38, no 5, p. 2708-2714Article in journal (Refereed)
    Abstract [en]

    Purpose: Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET/MRI applications, as well as for patient positioning and dose planning in MRI based radiation therapy workflows. This study presents a method for generating a drop in substitute for a CT image from a set of magnetic resonance (MR)images.

    Methods:A Gaussian mixture regression model was used to link the voxel values in CT images to the voxel values in images from three MRI sequences: one T2 weighted 3D spin echo based sequence and two dual echo ultrashort echo time MRI sequences with different echo times and flip angles. The method used a training set of matched MR and CT data that after training was able to predict a substitute CT (s-CT) based entirely on the MR information for a new patient. Method validation was achieved using datasets covering the heads of five patients and applying leave-one-out cross-validation (LOOCV). During LOOCV, the model was estimated from the MR and CT data of four patients (training set) and applied to the MR data of the remaining patient (validation set) to generate an s-CT image. This procedure was repeated for all five training and validation data combinations.

    Results: The mean absolute error for the CT number in the s-CT images was 137 HU. No large differences in method accuracy were noted for the different patients, indicating a robust method. The largest errors in the s-CT images were found at air–tissue and bone–tissue interfaces. The model accurately discriminated between air and bone, as well as between soft tissues and nonsoft tissues.

    Conclusions: The s-CT method has the potential to provide an accurate estimation of CT information without risk of geometrical inaccuracies as the model is voxel based. Therefore, s-CT images could be well suited as alternatives to CT images for dose planning in radiotherapy and attenuation correction in PET/MRI.

  • 10.
    Johansson, Adam
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Voxel-wise uncertainty in CT substitute derived from MRI2012In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 39, no 6, p. 3283-3290Article in journal (Refereed)
    Abstract [en]

    Purpose: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences.

    Methods: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities.

    Results: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation.

    Conclusions: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.

  • 11.
    Jonsson, Joakim H.
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Akhtari, Mohammad M.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Karlsson, Magnus G.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Johansson, Adam
    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.
    Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions2015In: Radiation Oncology, ISSN 1748-717X, E-ISSN 1748-717X, Vol. 10, article id 13Article in journal (Refereed)
    Abstract [en]

    Background: In this pilot study we evaluated the performance of a substitute CT (s-CT) image derived from MR data of the brain, as a basis for optimization of intensity modulated rotational therapy, final dose calculation and derivation of reference images for patient positioning. Methods: S-CT images were created using a Gaussian mixture regression model on five patients previously treated with radiotherapy. Optimizations were compared using D-max, D-min, D-median and D-mean measures for the target volume and relevant risk structures. Final dose calculations were compared using gamma index with 1%/1 mm and 3%/3 mm acceptance criteria. 3D geometric evaluation was conducted using the DICE similarity coefficient for bony structures. 2D geometric comparison of digitally reconstructed radiographs (DRRs) was performed by manual delineation of relevant structures on the s-CT DRR that were transferred to the CT DRR and compared by visual inspection. Results: Differences for the target volumes in optimization comparisons were small in general, e.g. a mean difference in both D-min and D-max within similar to 0.3%. For the final dose calculation gamma evaluations, 100% of the voxels passed the 1%/1 mm criterion within the PTV. Within the entire external volume between 99.4% and 100% of the voxels passed the 3%/3 mm criterion. In the 3D geometric comparison, the DICE index varied between approximately 0.8-0.9, depending on the position in the skull. In the 2D DRR comparisons, no appreciable visual differences were found. Conclusions: Even though the present work involves a limited number of patients, the results provide a strong indication that optimization and dose calculation based on s-CT data is accurate regarding both geometry and dosimetry.

  • 12.
    Jonsson, Joakim
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Treatment planning of intracranial targets on MRI derived substitute CT data2013In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 108, no 1, p. 118-122Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To eliminate systematic uncertainties due to image registration, a workflow based entirely on MRI may be preferable. In the present pilot study, we investigate dose calculation accuracy for automatically generated substitute CT (s-CT) images of the head based on MRI. We also produce digitally reconstructed radiographs (DRRs) from s-CT data to evaluate the feasibility of patient positioning based on MR images. METHODS AND MATERIALS: Five patients were included in the study. The dose calculation was performed on CT, s-CT, s-CT data without inhomogeneity correction and bulk density assigned MRI images. Evaluation of the results was performed using point dose and dose volume histogram (DVH) comparisons, and gamma index evaluation. RESULTS: The results demonstrate that the s-CT images improves the dose calculation accuracy compared to the method of non-inhomogeneity corrected dose calculations (mean improvement 2.0 percentage points) and that it performs almost identically to the method of bulk density assignment. The s-CT based DRRs appear to be adequate for patient positioning of intra-cranial targets, although further investigation is needed on this subject. CONCLUSIONS: The s-CT method is very fast and yields data that can be used for treatment planning without sacrificing accuracy.

  • 13.
    Larsson, Anne
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images2013In: Magnetic Resonance Materials in Physics, Biology and Medicine, ISSN 0968-5243, E-ISSN 1352-8661, Vol. 26, no 1, p. 127-136Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to evaluate MR-based attenuation correction of PET emission data of the head, based on a previously described technique that calculates substitute CT (sCT) images from a set of MR images. Images from eight patients, examined with F-18-FLT PET/CT and MRI, were included. sCT images were calculated and co-registered to the corresponding CT images, and transferred to the PET/CT scanner for reconstruction. The new reconstructions were then compared with the originals. The effect of replacing bone with soft tissue in the sCT-images was also evaluated. The average relative difference between the sCT-corrected PET images and the CT-corrected PET images was 1.6 % for the head and 1.9 % for the brain. The average standard deviations of the relative differences within the head were relatively high, at 13.2 %, primarily because of large differences in the nasal septa region. For the brain, the average standard deviation was lower, 4.1 %. The global average difference in the head when replacing bone with soft tissue was 11 %. The method presented here has a high rate of accuracy, but high-precision quantitative imaging of the nasal septa region is not possible at the moment.

  • 14.
    Lindström, Anton
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Andersson, C. David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters2013In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 125, p. 33-39Article in journal (Refereed)
    Abstract [en]

    For the purpose of improved planning and treatment by radiation of tumours, we present work exploring the effect of controllable ultra-short echo-time (UTE) sequence settings on the bone contrast in magnetic resonance (MR) imaging, using design of experiments (DoE). Images were collected using UTE sequences from MR imaging and from standard computed tomography (CT). CT was used for determining the spatial position of the bony structures in an animal sample and co-registered with the MR images. The effect of the UTE sequence parameter flip angle (Flip), repetition time (T-R), echo time (T-E), image matrix size (Vox) and number of radial sampling spokes (Samp) were studied. The parameters were also investigated in a healthy voluntary and it was determined that the optimal UTE settings for high bone contrast in a clinically relevant set up were: Flip similar to 9 degrees and T-E = 0.07 ms, while T-R was kept at 8 ms, Vox at 192 and Samp at 30,000. The use of response surface maps, describing the modelled relation between bone contrast and UTE settings, founded in the DoE, may provide information and be a tool to more appropriately select suitable UTE sequence settings.

  • 15.
    Lundman, Josef A.
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Effect of gradient field nonlinearity distortions in MRI-based attenuation maps for PET reconstruction2017In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 35, p. 1-6Article in journal (Refereed)
    Abstract [en]

    Purpose: Attenuation correction is a requirement for quantification of the activity distribution in PET. The need to base attenuation correction on MRI instead of CT has arisen with the introduction of integrated PET/MRI systems. The aim was to describe the effect of residual gradient field nonlinearity distortions on PET attenuation correction.

    Methods: MRI distortions caused by gradient field nonlinearity were simulated in CT images used for attenuation correction in PET reconstructions. The simulations yielded radial distortion of up to  at 15 cm from the scanner isocentre for distortion corrected images. The mean radial distortion of uncorrected images were 6.3 mm at the same distance. Reconstructions of PET data were performed using the distortion corrected images as well as the images where no correction had been applied.

    Results: The mean relative difference in reconstructed PET uptake intensity due to incomplete distortion correction was less than ±5%. The magnitude of this difference varied between patients and the size of the distortions remaining after distortion correction.

    Conclusions: Radial distortions of 2 mm at 15 cm radius from the scanner isocentre lead to PET attenuation correction errors smaller than 5%. Keeping the gradient field nonlinearity distortions below this limit can be a reasonable goal for MRI systems used for attenuation correction in PET for quantification purposes. A higher geometrical accuracy may, however, be warranted for quantification of peripheral lesions. These distortions can, e.g., be controlled at acceptance testing and subsequent quality assurance intervals.

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