Zeitschrift fur Medizinische Physik最新文献

{"title":"Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow","authors":"","doi":"10.1016/j.zemedi.2022.12.004","DOIUrl":"10.1016/j.zemedi.2022.12.004","url":null,"abstract":"<div><p>This work proposes a method for automatic standardized assessment of bone marrow volume and spatial distribution of the proton density fat fraction (PDFF) in vertebral bodies. Intra- and interindividual variability in size and shape of vertebral bodies is a challenge for comparable interindividual evaluation and monitoring of changes in the composition and distribution of bone marrow due to aging and/or intervention. Based on deep learning image segmentation, bone marrow PDFF of single vertebral bodies is mapped to a cylindrical template and corrected for the inclination with respect to the horizontal plane. The proposed technique was applied and tested in a cohort of 60 healthy (30 males, 30 females) individuals. Obtained bone marrow volumes and mean PDFF values are comparable to former manual and (semi-)automatic approaches. Moreover, the proposed method allows shape-independent characterization of the spatial PDFF distribution inside vertebral bodies.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 436-445"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388922001374/pdfft?md5=3a58aa031c20a8862a2b4ccbc7e4eacc&pid=1-s2.0-S0939388922001374-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10602821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and clinical implementation of a digital system for risk assessments for radiation therapy","authors":"","doi":"10.1016/j.zemedi.2023.08.003","DOIUrl":"10.1016/j.zemedi.2023.08.003","url":null,"abstract":"<div><p>Before introducing new treatment techniques, an investigation of hazards due to unintentional radiation exposures is a reasonable activity for proactively increasing patient safety. As dedicated software is scarce, we developed a tool for risk assessment to design a quality management program based on best practice methods, i.e., process mapping, failure modes and effects analysis and fault tree analysis. Implemented as a web database application, a single dataset was used to describe the treatment process and its failure modes. The design of the system and dataset allowed failure modes to be represented both visually as fault trees and in a tabular form. Following the commissioning of the software for our department, previously conducted risk assessments were migrated to the new system after being fully re-assessed which revealed a shift in risk priorities. Furthermore, a weighting factor was investigated to bring risk levels of the migrated assessments into perspective. The compensation did not affect high priorities but did re-prioritize in the midrange of the ranking. We conclude that the tool is suitable to conduct multiple risk assessments and concomitantly keep track of the overall quality management activities.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 371-383"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000922/pdfft?md5=2c3be2cbda2c97bb47ce7e968801481d&pid=1-s2.0-S0939388923000922-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10153447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process failure mode and effects analysis for external beam radiotherapy: Introducing a literature-based template and a novel action priority","authors":"","doi":"10.1016/j.zemedi.2024.02.002","DOIUrl":"10.1016/j.zemedi.2024.02.002","url":null,"abstract":"<div><h3>Purpose</h3><p>The first aim of the study was to create a general template for analyzing potential failures in external beam radiotherapy, EBRT, using the process failure mode and effects analysis (PFMEA). The second aim was to modify the action priority (AP), a novel prioritization method originally introduced by the Automotive Industry Action Group (AIAG), to work with different severity, occurrence, and detection rating systems used in radiation oncology.</p></div><div><h3>Methods and materials</h3><p>The AIAG PFMEA approach was employed in combination with an extensive literature survey to develop the EBRT-PFMEA template. Subsets of high-risk failure modes found through the literature survey were added to the template where applicable. Our modified AP for radiation oncology (RO AP) was defined using a weighted sum of severity, occurrence, and detectability. Then, Monte Carlo simulations were conducted to compare the original AIAG AP, the RO AP, and the risk priority number (RPN). The results of the simulations were used to determine the number of additional corrective actions per failure mode and to parametrize the RO AP to our department’s rating system.</p></div><div><h3>Results</h3><p>An EBRT-PFMEA template comprising 75 high-risk failure modes could be compiled. The AIAG AP required 1.7 additional corrective actions per failure mode, while the RO AP ranged from 1.3 to 3.5, and the RPN required 3.6. The RO AP could be parametrized so that it suited our rating system and evaluated severity, occurrence, and detection ratings equally to the AIAG AP.</p></div><div><h3>Conclusions</h3><p>An adjustable EBRT-PFMEA template is provided which can be used as a practical starting point for creating institution-specific templates. Moreover, the RO AP introduces transparent action levels that can be adapted to any rating system.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 358-370"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388924000254/pdfft?md5=d120d40663aca813c9baaed6b47b4dc7&pid=1-s2.0-S0939388924000254-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140013892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population-based model selection for an accurate estimation of time-integrated activity using non-linear mixed-effects modelling","authors":"","doi":"10.1016/j.zemedi.2023.01.007","DOIUrl":"10.1016/j.zemedi.2023.01.007","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;p&gt;Personalized treatment planning in Molecular Radiotherapy (MRT) with accurately determining the absorbed dose is highly desirable. The absorbed dose is calculated based on the Time-Integrated Activity (TIA) and the dose conversion factor. A crucial unresolved issue in MRT dosimetry is which fit function to use for the TIA calculation. A data-driven population-based fitting function selection could help solve this problem. Therefore, this project aims to develop and evaluate a method for accurately determining TIAs in MRT, which performs a Population-Based Model Selection within the framework of the Non-Linear Mixed-Effects (NLME-PBMS) model.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;p&gt;Biokinetic data of a radioligand for the Prostate-Specific Membrane Antigen (PSMA) for cancer treatment were used. Eleven fit functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted (in the NLME framework) to the biokinetic data of all patients. The goodness of fit was assumed acceptable based on the visual inspection of the fitted curves and the coefficients of variation of the fitted fixed effects. The Akaike weight, the probability that the model is the best among the whole set of considered models, was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. NLME-PBMS Model Averaging (MA) was performed with all functions having acceptable goodness of fit. The Root-Mean-Square Error (RMSE) of the calculated TIAs from individual-based model selection (IBMS), a shared-parameter population-based model selection (SP-PBMS) reported in the literature, and the functions from NLME-PBMS method to the TIAs from MA were calculated and analysed. The NLME-PBMS (MA) model was used as the reference as this model considers all relevant functions with corresponding Akaike weights.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;p&gt;The function &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;mfenced&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt;&lt;mo&gt;+&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;phys&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/mfenced&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;+&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;mfenced&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;phys&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/mfenced&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; was selected as the function most supported by the data with an Akaike weight of (54 ± 11) %. Visual inspection of the fitted graphs and the RMSE values show that the NLME model selection method has a relatively better or equivalent performance than the IBMS or SP-PBMS methods. The RMSEs of the IBMS, SP-PBMS, and NLME-PBMS (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) methods are 7.4%, 8.8%, an","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 419-427"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000077/pdfft?md5=74c506ab31c26e269984eefabd8f12a8&pid=1-s2.0-S0939388923000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10816823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing Off-center MRI with ZTE","authors":"","doi":"10.1016/j.zemedi.2022.09.002","DOIUrl":"10.1016/j.zemedi.2022.09.002","url":null,"abstract":"<div><h3>Purpose</h3><p>To maximize acquisition bandwidth in zero echo time (ZTE) sequences, readout gradients are already switched on during the RF pulse, creating unwanted slice selectivity. The resulting image distortions are amplified especially when the anatomy of interest is not located at the isocenter. We aim to characterize off-center ZTE MRI of extremities such as the shoulder, knee, and hip, adjusting the carrier frequency of the RF pulse excitation for each TR.</p></div><div><h3>Methods</h3><p>In ZTE MRI, radial encoding schemes are used, where the distorted slice profile due to the finite RF pulse length rotates with the k-space trajectory. To overcome these modulations for objects far away from the magnet isocenter, the frequency of the RF pulse is shifted for each gradient setting so that artifacts do not occur at a given off-center target position. The sharpness of the edges in the images were calculated and the ZTE acquisition with off-center excitation was compared to an acquisition with isocenter excitation both in phantom and <em>in vivo</em> off-center MRI of the shoulder, knee, and hip at 1.5 and 3T MRI systems.</p></div><div><h3>Results</h3><p>Distortion and blurriness artifacts on the off-center MRI images of the phantom, <em>in vivo</em> shoulder, knee, and hip images were mitigated with off-center excitation without time or noise penalty, at no additional computational cost.</p></div><div><h3>Conclusion</h3><p>The off-center excitation allows ZTE MRI of the shoulder, knee, and hip for high-bandwidth image acquisitions for clinical settings, where positioning at the isocenter is not possible.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 446-455"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388922000940/pdfft?md5=88ef93407ba6cb137dc9ea24421d0b25&pid=1-s2.0-S0939388922000940-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40679315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A body mass index-based method for “MR-only” abdominal MR-guided adaptive radiotherapy","authors":"","doi":"10.1016/j.zemedi.2022.12.001","DOIUrl":"10.1016/j.zemedi.2022.12.001","url":null,"abstract":"<div><h3>Purpose</h3><p>Dose calculation for MR-guided radiotherapy (MRgRT) at the 0.35 T MR-Linac is currently based on deformation of planning CTs (defCT) acquired for each patient. We present a simple and robust bulk density overwrite synthetic CT (sCT) method for abdominal treatments in order to streamline clinical workflows.</p></div><div><h3>Method</h3><p>Fifty-six abdominal patient treatment plans were retrospectively evaluated. All patients had been treated at the MR-Linac using MR datasets for treatment planning and plan adaption and defCT for dose calculation. Bulk density CTs (4M-sCT) were generated from MR images with four material compartments (bone, lung, air, soft tissue). The relative electron densities (RED) for bone and lung were extracted from contoured CT structure average REDs. For soft tissue, a correlation between BMI and RED was evaluated. Dose was recalculated on 4M-sCT and compared to dose distributions on defCTs assessing dose differences in the PTV and organs at risk (OAR).</p></div><div><h3>Results</h3><p>Mean RED of bone was 1.17 ± 0.02, mean RED of lung 0.17 ± 0.05. The correlation between BMI and RED for soft tissue was statistically significant (p &lt; 0.01). PTV dose differences between 4M-sCT and defCT were D_mean: −0.4 ± 1.0%, D_1%: −0.3 ± 1.1% and D_95%: −0.5 ± 1.0%. OARs showed D_2%: −0.3 ± 1.9% and D_mean: −0.1 ± 1.4% differences. Local 3D gamma index pass rates (2%/2mm) between dose calculated using 4M-sCT and defCT were 96.8 ± 2.6% (range 89.9–99.6%).</p></div><div><h3>Conclusion</h3><p>The presented method for sCT generation enables precise dose calculation for MR-only abdominal MRgRT.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 456-467"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388922001349/pdfft?md5=96a02bfed77ffd9d1bc9385391487bd1&pid=1-s2.0-S0939388922001349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10742581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re-evaluation of the prospective risk analysis for artificial-intelligence driven cone beam computed tomography-based online adaptive radiotherapy after one year of clinical experience","authors":"","doi":"10.1016/j.zemedi.2024.05.001","DOIUrl":"10.1016/j.zemedi.2024.05.001","url":null,"abstract":"<div><p>Cone-beam computed tomography (CBCT)-based online adaptation is increasingly being introduced into many clinics. Upon implementation of a new treatment technique, a prospective risk analysis is required and enhances workflow safety. We conducted a risk analysis using Failure Mode and Effects Analysis (FMEA) upon the introduction of an online adaptive treatment programme (Wegener et al., Z Med Phys. 2022).</p><p>A prospective risk analysis, lacking in-depth clinical experience with a treatment modality or treatment machine, relies on imagination and estimates of the occurrence of different failure modes. Therefore, we systematically documented all irregularities during the first year of online adaptation, namely all cases in which quality assurance detected undesired states potentially leading to negative consequences. Additionally, the quality of automatic contouring was evaluated. Based on those quantitative data, the risk analysis was updated by an interprofessional team. Furthermore, a hypothetical radiation therapist-only workflow during adaptive sessions was included in the prospective analysis, as opposed to the involvement of an interprofessional team performing each adaptive treatment.</p><p>A total of 126 irregularities were recorded during the first year. During that time period, many of the previously anticipated failure modes (almost) occurred, indicating that the initial prospective risk analysis captured relevant failure modes. However, some scenarios were not anticipated, emphasizing the limits of a prospective risk analysis. This underscores the need for regular updates to the risk analysis. The most critical failure modes are presented together with possible mitigation strategies. It was further noted that almost half of the reported irregularities applied to the non-adaptive treatments on this treatment machine, primarily due to a manual plan import step implemented in the institution’s workflow.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 397-407"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388924000497/pdfft?md5=45dc81fc3a80f7dc5d71e12b69c8edca&pid=1-s2.0-S0939388924000497-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141294041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective risk analysis of the online-adaptive artificial intelligence-driven workflow using the Ethos treatment system","authors":"","doi":"10.1016/j.zemedi.2022.11.004","DOIUrl":"10.1016/j.zemedi.2022.11.004","url":null,"abstract":"<div><h3>Purpose</h3><p>The recently introduced Varian Ethos system allows adjusting radiotherapy treatment plans to anatomical changes on a daily basis. The system uses artificial intelligence to speed up the process of creating adapted plans, comes with its own software solutions and requires a substantially different workflow. A detailed analysis of possible risks of the associated workflow is presented.</p></div><div><h3>Methods</h3><p>A prospective risk analysis of the adaptive workflow with the Ethos system was performed using Failure Modes and Effects Analysis (FMEA). An interprofessional team collected possible adverse events and evaluated their severity as well as their chance of occurrence and detectability. Measures to reduce the risks were discussed.</p></div><div><h3>Results</h3><p>A total of 122 events were identified, and scored. Within the 20 events with the highest-ranked risks, the following were identified: Challenges due to the stand-alone software solution with very limited connectivity to the existing record and verify software and digital patient file, unfamiliarity with the new software and its limitations and the adaption process relying on results obtained by artificial intelligence. The risk analysis led to the implementation of additional quality assurance measures in the workflow.</p></div><div><h3>Conclusions</h3><p>The thorough analysis of the risks associated with the new treatment technique was the basis for designing details of the workflow. The analysis also revealed challenges to be addressed by both, the vendor and customers. On the vendor side, this includes improving communication between their different software solutions. On the customer side, this especially includes establishing validation strategies to monitor the results of the black box adaption process making use of artificial intelligence.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Pages 384-396"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388922001210/pdfft?md5=8005a35cd84cb01da2103d84e31bf1aa&pid=1-s2.0-S0939388922001210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10746123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Board + Consulting Editorial Board","authors":"","doi":"10.1016/S0939-3889(24)00060-6","DOIUrl":"10.1016/S0939-3889(24)00060-6","url":null,"abstract":"","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 3","pages":"Page iii"},"PeriodicalIF":2.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388924000606/pdfft?md5=863bfbdd09bc5d7c2602ad7969b53faa&pid=1-s2.0-S0939388924000606-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing","authors":"Daniel Güllmar ,&nbsp;Wei-Chan Hsu ,&nbsp;Jürgen R. Reichenbach","doi":"10.1016/j.zemedi.2023.12.001","DOIUrl":"10.1016/j.zemedi.2023.12.001","url":null,"abstract":"<div><h3>Introduction</h3><p>Multiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI).</p></div><div><h3>Methods</h3><p>We trained the StyleGAN model unsupervised using T₁-weighted GRE MR images and diffusion-based ADC maps of MS patients and healthy controls. We then used the trained model to resample MR images from real input data and modified them by manipulations in the latent space to simulate MS progression. We analyzed the resulting simulation-related patterns mimicking disease progression by comparing the intensity profiles of the original and manipulated images and determined the brain parenchymal fraction (BPF).</p></div><div><h3>Results</h3><p>Our results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T₁-weighted and ADC maps and increasing lesion extent on ADC maps.</p></div><div><h3>Conclusion</h3><p>Overall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Pages 318-329"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923001484/pdfft?md5=41054e941858901ec78e1d44ca3d8f6d&pid=1-s2.0-S0939388923001484-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139030422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}