Publications by authors named "Maja Sohlin"

8 Publications

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Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy.

Phys Imaging Radiat Oncol 2021 Jan 11;17:36-42. Epub 2021 Jan 11.

Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Background And Purpose: Few studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm.

Materials And Methods: For 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume.

Results: For overall body, the results of the geometric validation were (Mean ± 1sd): Mean error -5 ± 10 HU, mean absolute error 67 ± 14 HU, Dice similarity coefficient 0.98 ± 0.05, and Hausdorff distance difference 4.2 ± 1.7 mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were -0.3 ± 3.4, 1.1 ± 2.0 and 0.7 ± 3.8 mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12 Gy). The absorbed doses were considered equivalent (p-value < 0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7-99.9%).

Conclusions: The convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.
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http://dx.doi.org/10.1016/j.phro.2020.12.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058030PMC
January 2021

Adipose tissue morphology, imaging and metabolomics predicting cardiometabolic risk and family history of type 2 diabetes in non-obese men.

Sci Rep 2020 06 19;10(1):9973. Epub 2020 Jun 19.

The Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.

We evaluated the importance of body composition, amount of subcutaneous and visceral fat, liver and heart ectopic fat, adipose tissue distribution and cell size as predictors of cardio-metabolic risk in 53 non-obese male individuals. Known family history of type 2 diabetes was identified in 25 individuals. The participants also underwent extensive phenotyping together with measuring different biomarkers and non-targeted serum metabolomics. We used ensemble learning and other machine learning approaches to identify predictors with considerable relative importance and their intricate interactions. Visceral fat and age were strong individual predictors of ectopic fat accumulation in liver and heart along with markers of lipid oxidation and reduced glucose tolerance. Subcutaneous adipose cell size was the strongest individual predictor of whole-body insulin sensitivity and also a marker of visceral and ectopic fat accumulation. The metabolite 3-MOB along with related branched-chain amino acids demonstrated strong predictability for family history of type 2 diabetes.
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http://dx.doi.org/10.1038/s41598-020-66199-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305301PMC
June 2020

MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.

Med Phys 2018 Mar 24;45(3):1295-1300. Epub 2018 Jan 24.

Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden.

Purpose: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT).

Acquisition And Validation Methods: T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset.

Data Format And Usage Notes: The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096.

Potential Applications: The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm.
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http://dx.doi.org/10.1002/mp.12748DOI Listing
March 2018

MR-OPERA: A Multicenter/Multivendor Validation of Magnetic Resonance Imaging-Only Prostate Treatment Planning Using Synthetic Computed Tomography Images.

Int J Radiat Oncol Biol Phys 2017 11 16;99(3):692-700. Epub 2017 Jun 16.

Department of Radiation Sciences, Umeå University, Umeå, Sweden.

Purpose: To validate the dosimetric accuracy and clinical robustness of a commercially available software for magnetic resonance (MR) to synthetic computed tomography (sCT) conversion, in an MR imaging-only workflow for 170 prostate cancer patients.

Methods And Materials: The 4 participating centers had MriPlanner (Spectronic Medical), an atlas-based sCT generation software, installed as a cloud-based service. A T2-weighted MR sequence, covering the body contour, was added to the clinical protocol. The MR images were sent from the MR scanner workstation to the MriPlanner platform. The sCT was automatically returned to the treatment planning system. Four MR scanners and 2 magnetic field strengths were included in the study. For each patient, a CT-treatment plan was created and approved according to clinical practice. The sCT was rigidly registered to the CT, and the clinical treatment plan was recalculated on the sCT. The dose distributions from the CT plan and the sCT plan were compared according to a set of dose-volume histogram parameters and gamma evaluation. Treatment techniques included volumetric modulated arc therapy, intensity modulated radiation therapy, and conventional treatment using 2 treatment planning systems and different dose calculation algorithms.

Results: The overall (multicenter/multivendor) mean dose differences between sCT and CT dose distributions were below 0.3% for all evaluated organs and targets. Gamma evaluation showed a mean pass rate of 99.12% (0.63%, 1 SD) in the complete body volume and 99.97% (0.13%, 1 SD) in the planning target volume using a 2%/2-mm global gamma criteria.

Conclusions: Results of the study show that the sCT conversion method can be used clinically, with minimal differences between sCT and CT dose distributions for target and relevant organs at risk. The small differences seen are consistent between centers, indicating that an MR imaging-only workflow using MriPlanner is robust for a variety of field strengths, vendors, and treatment techniques.
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http://dx.doi.org/10.1016/j.ijrobp.2017.06.006DOI Listing
November 2017

The influence of cardiac triggering time and an optimization strategy for improved cardiac MR spectroscopy.

Z Med Phys 2017 Dec 26;27(4):310-317. Epub 2017 May 26.

Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Radiation Physics, Gothenburg University, Gothenburg, Sweden.

Purpose: To study how cardiac motion affects the spectral quality in cardiac MR spectroscopy and to establish an optimization strategy for the cardiac triggering time for improved quality and success rate of cardiac MRS.

Method: Water spectra were acquired while the cardiac triggering time was varied over the cardiac cycle, and five different spectral quality parameters were studied (frequency, phase, linewidth, amplitude and noise). Furthermore, three different optimization strategies for the cardiac triggering time were tested, and finally, a comparison was made between water suppressed lipid spectra acquired in systole and diastole.

Results: The cardiac triggering time had a high impact on the spectral quality, especially on the mean signal amplitude and the standard deviation of the signal amplitude, phase and linewidth. Generally, the highest spectral quality was observed for spectra acquired in mid to end systole, at approximately 23% of the cardiac cycle. The exact optimal triggering time differed between subjects and needed to be individually optimized. To optimize the triggering time with our proposed MRS-method gave in average 13% higher signal than when the triggering time was determined through imaging. Lipid spectra acquired in systole demonstrated higher quality with improved SNR compared with acquisitions made in diastole.

Conclusion: This study shows that the spectral quality in cardiac MRS is strongly dependent on the cardiac triggering time, and that the spectral quality as well as the repeatability between acquisitions is greatly improved when the cardiac triggering time is individually optimized in mid to end systole using MRS.
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http://dx.doi.org/10.1016/j.zemedi.2017.04.006DOI Listing
December 2017

Metabolic effects of Lactobacillus reuteri DSM 17938 in people with type 2 diabetes: A randomized controlled trial.

Diabetes Obes Metab 2017 04 7;19(4):579-589. Epub 2017 Feb 7.

Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.

Aims: To investigate the metabolic effects of 12-week oral supplementation with Lactobacillus reuteri DSM 17938 in patients with type 2 diabetes on insulin therapy.

Materials And Methods: In a double-blind trial, we randomized 46 people with type 2 diabetes to placebo or a low (10  CFU/d) or high dose (10  CFU/d) of L. reuteri DSM 17938 for 12 weeks. The primary endpoint was the effect of supplementation on glycated haemoglobin (HbA1c). Secondary endpoints were insulin sensitivity (assessed by glucose clamp), liver fat content, body composition, body fat distribution, faecal microbiota composition and serum bile acids.

Results: Supplementation with L. reuteri DSM 17938 for 12 weeks did not affect HbA1c, liver steatosis, adiposity or microbiota composition. Participants who received the highest dose of L. reuteri exhibited increases in insulin sensitivity index (ISI) and serum levels of the secondary bile acid deoxycholic acid (DCA) compared with baseline, but these differences were not significant in the between-group analyses. Post hoc analysis showed that participants who responded with increased ISI after L. reuteri supplementation had higher microbial diversity at baseline, and increased serum levels of DCA after supplementation. In addition, increases in DCA levels correlated with improvement in insulin sensitivity in the probiotic recipients.

Conclusions: Intake of L. reuteri DSM 17938 for 12 weeks did not affect HbA1c in people with type 2 diabetes on insulin therapy; however, L. reuteri improved insulin sensitivity in a subset of participants and we propose that high diversity of the gut microbiota at baseline may be important.
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http://dx.doi.org/10.1111/dom.12861DOI Listing
April 2017

Numerical modeling of susceptibility-related MR signal dephasing with vessel size measurement: phantom validation at 3T.

Magn Reson Med 2014 Sep 24;72(3):646-58. Epub 2013 Oct 24.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA; Center for Imaging of Neurodegenerative Diseases, Department of Veterans Affairs Medical Center, San Francisco, California, USA.

Purpose: MRI is used to obtain quantitative oxygenation and blood volume information from the susceptibility-related MR signal dephasing induced by blood vessels. However, analytical models that fit the MR signal are usually not accurate over the range of small blood vessels. Moreover, recent studies have demonstrated limitations in the simultaneous assessment of oxygenation and blood volume. In this study, a multiparametric MRI framework that aims to measure vessel radii in addition to magnetic susceptibility and volume fraction was introduced.

Methods: The protocol consisted of gradient-echo sampling of the spin-echo, diffusion, T2, and B0 acquisitions. After correction steps, the data were postprocessed with a versatile numerical model of the MR signal. An important analytical model was implemented for comparison. The approach was validated in phantoms with coiling strings as proxy for blood vessels.

Results: The feasibility of the vessel radius measurement is demonstrated. The numerical model shows an improved accuracy compared with the analytical approach. However, both methods overestimate the radius. The simultaneous measurement of the magnetic susceptibility and the volume fraction remains challenging.

Conclusion: The results suggest that this approach could be interesting in vivo to better characterize the microvasculature without contrast agent.
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http://dx.doi.org/10.1002/mrm.24968DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3999331PMC
September 2014

Susceptibility-related MR signal dephasing under nonstatic conditions: experimental verification and consequences for qBOLD measurements.

J Magn Reson Imaging 2011 Feb;33(2):417-25

Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.

Purpose: To experimentally verify a theoretical model describing the MR signal dephasing under nonstatic conditions in a voxel containing a vascular network, and to estimate the stability of the model for qBOLD measurements.

Materials And Methods: Measurement phantoms reflecting the properties of the theoretical model, i.e., statistically distributed and randomly oriented cylinders in a homogeneous medium were constructed by randomly coiled polyamide fibers immersed in a NiSO(4) solution. The resemblance between measured and theoretical signal curves was investigated by calculation of root mean squared error maps. Simulated nonstatic dephasing data were evaluated using the static dephasing model to estimate the stability of the model and the influence of input parameters.

Results: The theoretical model describing the MR signal dephasing under nonstatic conditions was experimentally verified in phantom measurements. In simulations, it was found that, by neglecting the effect of diffusion when predicting the MR signal-time course expected in an in vivo measurement of the tissue oxygenation, errors of 10-30% would be introduced into the parameter estimation. The simulations indicate unpredictable results for simultaneous evaluation of blood oxygenation level and blood volume fraction.

Conclusion: Neglecting the effects of diffusion in quantitative BOLD measurements could give rise to substantial errors in the parameter estimation.
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http://dx.doi.org/10.1002/jmri.22423DOI Listing
February 2011