Medical physics最新文献

{"title":"Dose distributions of proton therapy plans are robust against lowering the resolution of CTs combined with increasing noise","authors":"Carla Frensch, Claus Maximilian Bäcker, Walter Jentzen, Ann-Kristin Lüvelsmeyer, Mohammadreza Teimoorisichani, Jörg Wulff, Beate Timmermann, Christian Bäumer","doi":"10.1002/mp.17530","DOIUrl":"10.1002/mp.17530","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Treatment planning in radiation therapy (RT) is performed on image sets acquired with commercial x-ray computed tomography (CT) scanners. Considering an increased frequency of verification scans for adaptive RT and the advent of alternatives to x-ray CTs, there is a need to review the requirements for image sets used in RT planning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to derive the required image quality (IQ) for the computation of the dose distribution in proton therapy (PT) regarding spatial resolution and the combination of spatial resolution and noise. The knowledge gained is used to explore the potential for dose reduction in tomography-guided PT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Mathematical considerations indicate that the required spatial resolution for dose computation is on the scale of the set-up margins fed into the robust optimization. This hypothesis was tested by processing retrospectively 12 clinical PT cases, which reflect a variety of tumor localizations. Image sets were low-pass filtered and were made noisy in a generic manner. Dose distributions on the modified CT scans were computed with a Monte-Carlo dose engine. The similarity of these dose distributions with clinical ones was quantified with the gamma-index (1 mm/1%). The potential reduction of the x-ray exposure compared to the planning CT scan was estimated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Dose distributions within the irradiated volume were robust against low-pass filtering of the CTs with kernels up to a full-width-at-half-maximum of 4 mm, that is, the gamma pass rate (1 mm/1%) was <span></span><math>\u0000 <semantics>\u0000 <mo>≥</mo>\u0000 <annotation>$ge$</annotation>\u0000 </semantics></math>98%. The limit of the filter width was 6 mm for brain tumors and 8 mm for targets in the abdomen. These pass rates remained approximately unchanged if a limited amount of noise was added to the CT image sets. The estimated potential reductions of the x-ray exposure were at least a factor of 20.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The requirements on IQ in terms of spatial resolution in combination with noise for computing the dose in PT are clearly lower than the IQ of current clinical planning. The results apply, for example, to ultra-low dose x-ray CTs, proton CTs with coarse spatial detection, and attenuati","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1293-1304"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid in vivo EPID image prediction using a combination of analytically calculated attenuation and AI predicted scatter","authors":"Brian Anderson, Lance Moore, Casey Bojechko","doi":"10.1002/mp.17549","DOIUrl":"10.1002/mp.17549","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The electronic portal imaging device (EPID) can be used in vivo, to detect on-treatment errors by evaluating radiation exiting a patient. To detect deviations from the planning intent, image predictions need to be modeled based on the patient's anatomy and plan information. To date in vivo transit images have been predicted using Monte Carlo (MC) algorithms. A deep learning approach can make predictions faster than MC and only requires patient information for training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To test the feasibility and reliability of creating a deep-learning model with patient data for predicting in vivo EPID images for IMRT treatments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In our approach, the in vivo EPID image was separated into contributions from primary and scattered photons. A primary photon attenuation function was determined by measuring attenuation factors for various thicknesses of solid water. The scatter component of in vivo EPID images was estimated using a convolutional neural network (CNN). The CNN input was a 3-channel image comprised of the non-transit EPID image and ray tracing projections through a pretreatment CBCT. The predicted scatter component was added to the primary attenuation component to give the full predicted in vivo EPID image. We acquired 193 IMRT fields/images from 93 patients treated on the Varian Halcyon. Model training:validation:test dataset ratios were 133:20:40 images. Additional patient plans were delivered to anthropomorphic phantoms, yielding 75 images for further validation. We assessed model accuracy by comparing model-calculated and measured in vivo images with a gamma comparison.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Comparing the model-calculated and measured in vivo images gives a mean gamma pass rate for the training:validation:test datasets of 95.4%:94.1%:92.9% for 3%/3 mm and 98.4%:98.4%:96.8% for 5%/3 mm. For images delivered to phantom data sets the average gamma pass rate was 96.4% (3%/3 mm criteria). In all data sets, the lower passing rates of some images were due to CBCT artifacts and patient motion that occurred between the time of CBCT and treatment. </p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The developed deep-learning-based model can generate in vivo EPID images with a mean gamma pass rate greater than 92% (3%/3 mm criteria). This approach provides an alternative to MC prediction algorithms. Image predictions can be made in 30 ms on a standard GPU. In future work, image predictions from this model can be ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1058-1069"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lung CT-based multi-lesion radiomic model to differentiate between nontuberculous mycobacteria and Mycobacterium tuberculosis","authors":"Yanlin Hu, Lingshan Zhong, Hongying Liu, Wenlong Ding, Li Wang, Zhiheng Xing, Liang Wan","doi":"10.1002/mp.17537","DOIUrl":"10.1002/mp.17537","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Nontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate them. However, existing radiomic methods mainly focus on specific lesion types, and have limitations in handling the presence of multiple lesion types that vary among different patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We aimed to establish a radiomic model based on multiple lesion types in the patient's CT scans, and analyzed the importance of different lesion types in distinguishing the two diseases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>120 NTM-LD and 120 MTB-LD patients were retrospectively enrolled in this study and randomly split into the training (168) and testing (72) sets. A total of 1037 radiomic features were extracted separately for each lesion type. The univariate analysis, least absolute shrinkage, and selection operator were used to select the significant radiomic features. The radiomic signature score (Radscore) from each lesion type was estimated and aggregated to construct the multi-lesion feature vector for each patient. A multi-lesion radiomic (MLR) model was then established using the random forest classifier, which can estimate importance coefficients for different lesion types. The performances of the MLR model and single radomic models were investigated by the receiver operating characteristic curve (ROC). The impact of the predicted lesion importance was also evaluated in subjective imaging diagnosis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The MLR model achieved an area under the curve (AUC) of 90.2% (95% CI: 86.2% 94.1%) in differentiating NTM-LD and MTB-LD, outperforming the models using specific lesion types following existing radiomic models by 1% to 13%. Among different lesion types, tree-in-bud pattern demonstrated the highest distinguishing value, followed by consolidation, nodules, and lymph node enlargement. Given the estimated lesion importance, two senior radiologists exhibited improved accuracy in diagnosis, with an increased accuracy of 8.33% and 8.34%, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This is the first radiomic study to use multiple lesion types to distinguish NTM-LD and MTB-LD. The developed MLR model performed well in differentiating the two diseases, and the lesion types with high importance exhibited the potential to assist experienced radiologists in clinical decision-making.</p>\u0000","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1086-1095"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging","authors":"Ming-feng Jiang,&nbsp;Yun-jiang Chen,&nbsp;Dong-sheng Ruan,&nbsp;Zi-han Yuan,&nbsp;Ju-cheng Zhang,&nbsp;Ling Xia","doi":"10.1002/mp.17501","DOIUrl":"10.1002/mp.17501","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low-rank core matrix and convolutional long short-term memory (ConvLSTM) unit.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state-of-the-art approaches, our approach achieves higher peak signal-to-noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 1","pages":"388-399"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-collimator proton minibeam radiotherapy with joint dose and PVDR optimization","authors":"Weijie Zhang,&nbsp;Xue Hong,&nbsp;Wei Wu,&nbsp;Chao Wang,&nbsp;Daniel Johnson,&nbsp;Gregory N. Gan,&nbsp;Yuting Lin,&nbsp;Hao Gao","doi":"10.1002/mp.17548","DOIUrl":"10.1002/mp.17548","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The clinical translation of proton minibeam radiation therapy (pMBRT) presents significant challenges, particularly in developing an optimal treatment planning technique. A uniform target dose is crucial for maximizing anti-tumor efficacy and facilitating the clinical acceptance of pMBRT. However, achieving a high peak-to-valley dose ratio (PVDR) in organs-at-risk (OAR) is essential for sparing normal tissue. This balance becomes particularly difficult when OARs are located distal to the beam entrance or require patient-specific collimators.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work proposes a novel pMBRT treatment planning method that can achieve high PVDR at OAR and uniform dose at target simultaneously, via multi-collimator pMBRT (MC-pMBRT) treatment planning method with joint dose and PVDR optimization (JDPO).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>MC-pMBRT utilizes a set of generic and premade multi-slit collimators with different center-to-center distances and does not need patient-specific collimators. The collimator selection per field is OAR-specific and tailored to maximize PVDR in OARs while preserving target dose uniformity. Then, the inverse optimization method JDPO is utilized to jointly optimize target dose uniformity, PVDR, and other dose-volume-histogram based dose objectives, which is solved by iterative convex relaxation optimization algorithm and alternating direction method of multipliers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The need and efficacy of MC-pMBRT is demonstrated by comparing the single-collimator (SC) approach with the multi-collimator (MC) approach. While SC degraded either PVDR for OAR or dose uniformity for the target, MC provided a good balance of PVDR and target dose uniformity. The proposed JDPO method is validated in comparison with the dose-only optimization (DO) method for MC-pMBRT, in reference to the conventional (CONV) proton RT (no pMBRT). Compared to CONV, MC-pMBRT (DO and JDPO) preserved target dose uniformity and plan quality, while providing unique PVDR in OAR. Compared to DO, JDPO further improved PVDR via PVDR optimization during treatment planning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A novel pMBRT treatment planning method called MC-pMBRT is proposed that utilizes a set of generic and premade collimators with joint dose and PVDR optimization algorithm to optimize OAR-specific PVDR and target dose uniformity simultaneously.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1182-1192"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution TOF-DOI PET detectors through the implementation of dual-ended readout with SiPM arrays of different pixel sizes on the two ends","authors":"Zheng Liu,&nbsp;Samuel Mungai,&nbsp;Zhonghua Kuang,&nbsp;Ning Ren,&nbsp;Siwei Xie,&nbsp;Qiyu Peng,&nbsp;Crispin Williams,&nbsp;Yongfeng Yang","doi":"10.1002/mp.17544","DOIUrl":"10.1002/mp.17544","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;An organ-specific Positron emission tomography (PET) scanner can achieve the same sensitivity with much fewer detectors as compared to a whole-body PET scanner, thereby substantially reducing the system cost. It can also achieve much better spatial resolution as compared to a whole-body PET scanner since the photon noncollinearity effect is reduced by using smaller detector ring diameter. Consequently, the development of organ-specific PET scanners with high spatial resolution, high sensitivity, and low cost has been a major focus of international research in PET instrument development for many years.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The focus of this work is to develop high-resolution depth encoding PET detectors with high timing resolution and a reduced number of signal processing electronic channels. Consequently, PET scanners tailored for specific organs can be developed with high spatial and timing resolutions, enhanced sensitivity, and affordable cost.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;An 8 × 8 silicon photomultiplier (SiPM) array with a pixel size of 3 × 3 mm&lt;sup&gt;2&lt;/sup&gt; and a multiplexed signal readout circuit is coupled to one end of the lutetium yttrium orthosilicate (LYSO) array with a glass light guide between them to achieve a good crystal identification of small crystals by using only four position-encoding energy signals. A 4 × 4 SiPM array with a pixel size of 6 × 6 mm&lt;sup&gt;2&lt;/sup&gt; and an individual readout circuit is coupled to the other end of the crystal array without a light guide to achieve a good coincidence timing resolution (CTR). The depth of interaction (DOI) of the detector is measured by ratio of the energies measured by the two SiPM arrays and can be used to correct the depth dependency of the timing. The flood histograms, energy resolutions (ERs), DOI resolutions, and CTRs of two detectors utilizing LYSO arrays with different crystal sizes were measured with each of the two SiPM arrays alternately placed at the front of the detectors.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A better flood histogram was obtained by placing the 8 × 8 SiPM array in front of the detector. The depth dependency of timing was larger when the 4 × 4 SiPM array was placed at the front of the detector. A better CTR was obtained by placing the 4 × 4 SiPM array at the back of the detector as compared to placing it at the front of the detector if the depth-dependent timing correction was not performed. If the depth-dependent timing correction was performed, a better CTR can be obtained by placing the 4 × 4 SiPM array at the front of the detector. The first detect","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"867-879"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of patient-specific pre-treatment verification procedure for FLASH proton therapy based on time resolved film dosimetry","authors":"K. H. Spruijt,&nbsp;J. Godart,&nbsp;M. Rovituso,&nbsp;Y. Wang,&nbsp;E. van der Wal,&nbsp;S. J. M. Habraken,&nbsp;M. Hoogeman","doi":"10.1002/mp.17534","DOIUrl":"10.1002/mp.17534","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Pre-clinical studies demonstrate that delivering a high dose at a high dose rate result in less toxicity while maintaining tumor control, known as the FLASH effect. In proton therapy, clinical trials have started using 250 MeV transmission beams and more trials are foreseen. A novel aspect of FLASH treatments, compared to conventional radiotherapy, is the importance of dose rate next to dose and geometry. Therefore, to ensure the safety and quality of FLASH treatments, patient-specific dose-rate verification before treatment is an important additional prerequisite. Various definitions of dose rate have been reported, however, the scanning proton beam (PBS) dose rate definition of Folkerts 2020 is currently the most used. It is the ratio between delta dose (ΔD) and delta time (Δt), subject to a predefined threshold, for a given position. Gafchromic film is a widely available detector used to perform relative and absolute integrated dose measurements. Since the response time of film is in the order of micro seconds it could also be suitable for pre-treatment verification of FLASH proton therapy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Development of a patient-specific pre-treatment verification procedure for FLASH proton therapy based on time resolved film dosimetry. The detector design is presented and validated using three tests.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A dedicated setup was built that holds a Gafchromic film and a high-speed camera to record the film during the irradiations. The red color channel of the camera's readings was converted into optical density (OD) and an OD-to-dose calibration curve was applied to determine the relative dose accumulation over time. To undo the film measurement (film response) of the post-irradiation coloration process, it is assumed that each dose deposit (pulse) results in a similar film response function. The convolution of the film response function over the pulse provides the film response. First the film response function was obtained by fitting this parameter onto a known film response and corresponding pulse. Post-irradiation coloration correction was performed by deconvoluting all film measurement by the obtained film response function. From the integral of each measured pulse, the Δt was obtained. Several validation tests were conducted: compare the Δt film measurement to a reference detector, exclude that revisiting spots result in an unwanted artefact on the dose accumulation measurement and thereby Δt, and compare Δt distributions of film measurement and simulation (local gamma evaluation, criteria 10%/2 mm) for nine QA fields (dose values","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1268-1280"},"PeriodicalIF":3.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142735438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-shaped windows transformer with self-supervised pretraining for clinically significant prostate cancer detection in bi-parametric MRI","authors":"Yuheng Li,&nbsp;Jacob Wynne,&nbsp;Jing Wang,&nbsp;Richard L. J. Qiu,&nbsp;Justin Roper,&nbsp;Shaoyan Pan,&nbsp;Ashesh B. Jani,&nbsp;Tian Liu,&nbsp;Pretesh R. Patel,&nbsp;Hui Mao,&nbsp;Xiaofeng Yang","doi":"10.1002/mp.17546","DOIUrl":"10.1002/mp.17546","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformers have achieved competitive performance compared to convolutional neural network (CNN) in deep learning, but they need abundant annotated data for training. Self-supervised learning can effectively leverage unlabeled data to extract useful semantic representations without annotation and its associated costs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study proposes a novel self-supervised learning framework and a transformer model to enhance PCa detection using prostate bpMRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and materials</h3>\u0000 \u0000 <p>We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bpMRI. We also propose a multitask self-supervised learning framework to leverage unlabeled data and improve network generalizability. Using a large prostate bpMRI dataset (PI-CAI) with 1476 patients, we first pretrain CSwin transformer using multitask self-supervised learning to improve data-efficiency and network generalizability. We then finetune using lesion annotations to perform csPCa detection. We also test the network generalization using a separate bpMRI dataset with 158 patients (Prostate158).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Five-fold cross validation shows that self-supervised CSwin UNet achieves 0.888 ± 0.010 aread under receiver operating characterstics curve (AUC) and 0.545 ± 0.060 Average Precision (AP) on PI-CAI dataset, significantly outperforming four comparable models (nnFormer, Swin UNETR, DynUNet, Attention UNet, UNet). On model generalizability, self-supervised CSwin UNet achieves 0.79 AUC and 0.45 AP, still outperforming all other comparable methods and demonstrating good generalization to external data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study proposes CSwin UNet, a new transformer-based model for end-to-end detection of csPCa, enhanced by self-supervised pretraining to enhance network generalizability. We employ an automatic weighted loss (AWL) to unify pretext tasks, improving representation learning. Evaluated on two multi-institutional public datasets, our method surpasses existing methods in detection metrics and demonstrates good generalization to external data.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"993-1004"},"PeriodicalIF":3.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strip and boundary detection multi-task learning network for segmentation of meibomian glands","authors":"Weifang Zhu,&nbsp;Dengfeng Liu,&nbsp;Xinyu Zhuang,&nbsp;Tian Gong,&nbsp;Fei Shi,&nbsp;Dehui Xiang,&nbsp;Tao Peng,&nbsp;Xiaofeng Zhang,&nbsp;Xinjian Chen","doi":"10.1002/mp.17542","DOIUrl":"10.1002/mp.17542","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Automatic segmentation of meibomian glands in near-infrared meibography images is basis of morphological parameter analysis, which plays a crucial role in facilitating the diagnosis of meibomian gland dysfunction (MGD). The special strip shape and the adhesion between glands make the automatic segmentation of meibomian glands very challenging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>A strip and boundary detection multi-task learning network (SBD-MTLNet) based on encoder-decoder structure is proposed to realize the automatic segmentation of meibomian glands.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A strip mixed attention module (SMAM) is proposed to enhance the network's ability to recognize the strip shape of glands. To alleviate the problem of adhesion between glands, a boundary detection auxiliary network (BDA-Net) is proposed, which introduces boundary features to assist gland segmentation. A self-adaptive interactive information fusion module (SIIFM) based on reverse attention mechanism is proposed to realize information complementation between meibomian gland segmentation and boundary detection tasks. The proposed SBD-MTLNet has been evaluated on an in-house dataset (453 images) and a public dataset MGD-1K (1000 images). Due to the limited number of images, a five-fold cross validation strategy is adopted.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Average dice coefficient of the proposed SBD-MTLNet reaches 81.08% and 84.32% on the in-house dataset and the public one, respectively. Comprehensive experimental results demonstrate the effectiveness the proposed SBD-MTLNet, outperforming other state-of-the-art methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed SBD-MTLNet can focus more on the shape characteristics of the meibomian glands and the boundary contour information between the adjacent glands via multi-task learning strategy. The segmentation results of the proposed method can be used for the quantitative morphological characteristics analysis of meibomian glands, which has potential for the auxiliary diagnosis of MGD in clinic.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1615-1628"},"PeriodicalIF":3.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and quantification of small and low-uptake lesions for differentiated thyroid carcinoma using non-time-of-flight iodine-124 PET/MRI","authors":"Maike E. Lindemann,&nbsp;Walter Jentzen,&nbsp;David Kersting,&nbsp;Pedro Fragoso Costa,&nbsp;Alina Küper,&nbsp;Lale Umutlu,&nbsp;Ken Herrmann,&nbsp;Harald H. Quick","doi":"10.1002/mp.17535","DOIUrl":"10.1002/mp.17535","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;124-iodine (&lt;sup&gt;124&lt;/sup&gt;I) is used for positron emission tomography (PET) diagnostics and therapy planning in patients with differentiated thyroid cancer (DTC). Small lesion sizes (&lt;10 mm) and low &lt;sup&gt;124&lt;/sup&gt;I uptake are challenging conditions for the detection of DTC lymph node lesions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The aim of this study was to systematically investigate the lesion detectability and quantification performance under clinically challenging imaging conditions using non-time-of-flight (TOF) PET/magnetic resonance imaging (MRI) in the clinical context of radionuclide therapy planning of DTC patients.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;PET/MR measurements were performed on the Siemens Biograph mMR using a small lesion NEMA-like phantom (six glass spheres, diameters 3.7–9.7 mm). 60 min list-mode data were acquired for nine activity concentrations (AC) ranging from 25 kBq/mL to 0.25 kBq/mL using a sphere-to-background ratio of 20:1. PET list-mode data were divided into five timeframes (60, 30, 16, 8, and 4 min) and reconstructed using either ordered-subsets expectation maximization (OSEM) or OSEM+ point spread function (PSF) algorithm. For all reconstructions, the smallest detectable sphere size was investigated in a human observer study. Partial volume effect (PVE) corrected PET images (contour and oversize-based approach) were analyzed considering a ± 30% deviation range between imaged and true AC as acceptable. Clinical data of eight DTC patients with small lymph node lesions were evaluated to assess agreement between the PVE correction approaches.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Longer PET acquisition times, higher ACs, and PSF reconstructions resulted in improved PET image quality and overall improved lesion detectability. The smallest 3.7 mm sphere was only visible under the best imaging conditions. Using a typical clinical &lt;sup&gt;124&lt;/sup&gt;I whole-body PET/MRI protocol with an acquisition time of 8 min using OSEM reconstructions, all lesions of ≥ 6.5 mm in diameter could be detected and the quantification provided reliable results approximately above 5.0 kBq/mL. An accurate quantification of ACs in the 4.8 mm sphere was not feasible in this study. In the clinical evaluation of 10 lesions, a good agreement between oversize- and contour-based PVE corrections was observed (&lt;15% deviation).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The resu","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"837-846"},"PeriodicalIF":3.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}