Medical physics最新文献

{"title":"Model observer task-based assessment of computed tomography metal artifact reduction using a hip arthroplasty phantom","authors":"Grant Fong, Steven Izen, Andrew Primak, Nancy Obuchowski, Wadih Karim, Brian Herts, Naveen Subhas","doi":"10.1002/mp.17817","DOIUrl":"10.1002/mp.17817","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The United States Food and Drug Administration (FDA) recently published a model observer-based framework for the objective performance assessment of computed tomography (CT) metal artifact reduction (MAR) algorithms and demonstrated the framework's feasibility in the low-contrast detectability (LCD) task-based assessment of MAR performance in a mathematical phantom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study investigates the feasibility of the model observer-based framework in LCD task-based assessment of MAR performance using a physical arthroplasty phantom, results of which were then compared with the performance of human observers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A phantom simulating a unilateral hip prosthesis was designed with a rotatable insert containing a metal implant (cobalt-chromium spheres attached to titanium rods) and 16 unique low-contrast spherical lesions. Each lesion was scanned 100 times on a CT scanner (Somatom Force, Siemens Healthineers) with standard full-dose and half-dose protocols (140 kVp, 300 and 150 quality reference mAs) in each of four different insert rotations to supply 100 pairs of signal-present (lesion) and signal-absent (background) images needed for model observer analyses. Lesion detectability (d′) using channelized Hotelling observers (CHO) was optimized by testing different image transformation techniques and channel selection (Gabor and Laguerre–Gauss [LG]) and calculated for each lesion reconstructed with and without iterative MAR (iMAR, Siemens Healthineers). Linear regression was used to assess the d′ in each image set. Spearman's correlation was used to compare d′ results to human detectability and confidence scores from a previously published human observer study involving the same phantom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>CHO d′ measurements using LG channels were less sensitive to artifacts than those using Gabor channels and were therefore selected for the LCD assessment. Image masking and thresholding provided more accurate d′ by isolating the signal and minimizing background differences. For all lesions, d′ values of full-dose iMAR images were significantly greater than those of filtered back projection (FBP) images at full dose (<i>p</i> < 0.001) and half dose (<i>p</i> < 0.001). Additionally, d′ values of half-dose iMAR images were significantly greater than those of FBP images at full dose (<i>p</i> = 0.010) and half dose (<i>p</i> < 0.001). The d′ values were not significantly different between full-dose ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3723-3733"},"PeriodicalIF":3.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048704","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":"Interplay-robust optimization for treating irregularly breathing lung patients with pencil beam scanning","authors":"Ivar Bengtsson,&nbsp;Anders Forsgren,&nbsp;Albin Fredriksson,&nbsp;Ye Zhang","doi":"10.1002/mp.17821","DOIUrl":"10.1002/mp.17821","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The steep dose gradients obtained with pencil beam scanning allow for precise targeting of the tumor but come at the cost of high sensitivity to uncertainties. Robust optimization is commonly applied to mitigate uncertainties in density and patient setup, while its application to motion management, called 4D-robust optimization (4DRO), is typically accompanied by other techniques, including gating, breath-hold, and re-scanning. In particular, current commercial implementations of 4DRO do not model the interplay effect between the delivery time structure and the patient's motion.</p>\u0000 \u0000 <p><b>Purpose</b>: Interplay-robust optimization (IPRO) has previously been proposed to explicitly model the interplay-affected dose during treatment planning. It has been demonstrated that IPRO can mitigate the interplay effect given the uncertainty in the patient's breathing frequency. In this study, we investigate and evaluate IPRO in the context where the motion uncertainty is extended to also include variations in breathing amplitude.</p>\u0000 \u0000 <p><b>Methods</b>: The compared optimization methods are applied and evaluated on a set of lung patients. We model the patients' motion using synthetic 4D computed tomography (s4DCT), each created by deforming a reference CT based on a motion pattern obtained with 4D magnetic resonance imaging. Each (s4DCT) contains multiple breathing cycles, partitioned into two sets for scenario generation: one for optimization and one for evaluation. Distinct patient motion scenarios are then created by randomly concatenating breathing cycles varying in period and amplitude. In addition, a method considering a single breathing cycle for generating optimization scenarios (IPRO-1C) is developed to investigate to which extent robustness can be achieved with limited information. Both IPRO and IPRO-1C were investigated with 9, 25, and 49 scenarios.</p>\u0000 <p><b>Results</b>: For all patient cases, IPRO and IPRO-1C increased the target coverage in terms of the near-worst-case (5th percentile) CTV D98, compared to 4DRO. After normalization of plan doses to equal target coverage, IPRO with 49 scenarios resulted in the greatest decreases in OAR dose, with near-worst-case (95th percentile) improvements averaging 4.2 %. IPRO-1C with 9 scenarios, with comparable computational demands as 4DRO, decreased OAR dose by 1.7 %.</p>\u0000 \u0000 <p><b>Conclusions</b>: The use of IPRO could lead to more efficient mitigation of the interplay effect, even when based on the information from a single breathing cycle. This can potentially decrease the need for real-time motion management techniques that prolong treatment times and decrease patient comfort.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3570-3582"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045461","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":"Semi-supervised medical image segmentation based on dual swap data mixing and cross EMA strategies","authors":"Licheng Zheng,&nbsp;Lihui Wang,&nbsp;Yingfeng Ou,&nbsp;Li Wang,&nbsp;Caiqing Jian,&nbsp;Yuemin Zhu","doi":"10.1002/mp.17809","DOIUrl":"10.1002/mp.17809","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;Semi-supervised medical image segmentation methods based on mean teacher (MT) framework provide a promising means for addressing the dense prediction problems with limited annotated images and numerous unlabeled images. However, the confirmation bias caused by the distribution difference between labeled and unlabeled data and the parameters-coupling problem of MT prevent the model from further improving the segmentation performance.&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;To reduce confirmation bias and alleviate the parameter coupling problem in MT framework, a novel data augmentation strategy and a cross exponential moving averaging (crossEMA) architecture are proposed in this work.&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;Specifically, a dual swap mixing data augmentation method was first proposed, which exchanges the patches between labeled and unlabeled images twice to decrease the confirmation bias caused by distribution divergency. Subsequently, a novel architecture for both student and teacher networks was designed with structurally identical dual decoders, one of which adopted a dropout operation. Labeled, unlabeled, and mixed images are fed into this MT architecture. For unlabeled data, the pseudo-labels generated by the dual decoders of the teacher network were used to supervise the predictions of the corresponding decoders of the student network. For mixed data, the real labels of the labeled data are mixed with the pseudo-labels of the unlabeled data predicted by the teacher network to form the supervisory information, which is used to constrain the prediction consistency for mixed data between student and teacher networks. To overcome the parameter coupling problem between the student and teacher networks, the encoder parameters of the teacher network were updated using an exponential moving average (EMA) strategy, while its dual decoder parameters were updated using a cross EMA strategy, which means the perturbed decoder parameters of the student network were updated with the non-perturbed decoder parameters of the student network and vice versa.&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;By comparing with several state-of-the-art (SOTA) semi-supervised segmentation methods on four publicly available datasets, we validated that the proposed method outperforms existing models. The Dice similarity coefficient (DSC) and volume similarity (VS) were improved by at least 2.33% and 1.86%, respectively, compared to the corresponding sub-optimal methods. Through multiple ablation experiments, we verified that the proposed dual swap strategy can reduce the distributional d","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4480-4497"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056169","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":"Breast Cancer: Habitat imaging based on intravoxel incoherent motion for predicting pathologic complete response to neoadjuvant chemotherapy","authors":"Hui Zhang,&nbsp;Yunyan Zheng,&nbsp;Mingzhe Zhang,&nbsp;Ailing Wang,&nbsp;Yang Song,&nbsp;Chenglong Wang,&nbsp;Guang Yang,&nbsp;Mingping Ma,&nbsp;Muzhen He","doi":"10.1002/mp.17813","DOIUrl":"10.1002/mp.17813","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;Radiomics research based on whole tumors is limited by the unclear biological significance of radiomics features, which therefore lack clinical interpretability.&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;We aimed to determine whether features extracted from subregions defined by habitat imaging, reflecting tumor heterogeneity, could identify breast cancer patients who will benefit from neoadjuvant chemotherapy (NAC), to optimize treatment.&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;143 women with stage II–III breast cancer were divided into a training set (100 patients, 36 with pathologic complete response [pCR]) and a test set (43 patients, 16 with pCR). Patients underwent 3-T magnetic resonance imaging (MRI) before NAC. With the pathological results as the gold standard, we used the training set to build models for predicting pCR based on whole-tumor radiomics (Model&lt;sub&gt;WH&lt;/sub&gt;), intravoxel incoherent motion (IVIM)-based habitat imaging (Model&lt;sub&gt;Habitats&lt;/sub&gt;), conventional MRI features (Model&lt;sub&gt;CF&lt;/sub&gt;), and immunohistochemical findings (Model&lt;sub&gt;IHC&lt;/sub&gt;). We also built the combined models Model&lt;sub&gt;Habitats+CF&lt;/sub&gt; and Model&lt;sub&gt;Habitats+CF+IHC&lt;/sub&gt;. In the test set, we compared the performance of the combined models with that of the invasive Model&lt;sub&gt;IHC&lt;/sub&gt; by using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of the model. The DeLong test was used to compare diagnostic efficiency across different parameters.&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;In the prediction of pCR, Model&lt;sub&gt;WH&lt;/sub&gt;, Model&lt;sub&gt;Habitats&lt;/sub&gt;, Model&lt;sub&gt;CF&lt;/sub&gt;, Model&lt;sub&gt;IHC&lt;/sub&gt;, Model&lt;sub&gt;Habitats+CF&lt;/sub&gt;, Model&lt;sub&gt;CF+IHC&lt;/sub&gt; and Model&lt;sub&gt;Habitats+CF+IHC&lt;/sub&gt; achieved AUCs of 0.895, 0.757, 0.705, 0.807, 0.800, 0.856, and 0.891 respectively, in the training set and 0.549, 0.708, 0.700, 0.788, 0.745, 0.909, and 0.891 respectively, in the test set. The DeLong test revealed no significant difference between Model&lt;sub&gt;IHC&lt;/sub&gt; versus Model&lt;sub&gt;Habitats+CF&lt;/sub&gt; (&lt;i&gt;p&lt;/i&gt; = 0.695) and Model&lt;sub&gt;Habitats+CF+IHC&lt;/sub&gt; versus Model&lt;sub&gt;CF+IHC&lt;/sub&gt; (&lt;i&gt;p&lt;/i&gt; = 0.382) but showed a significant difference between Model&lt;sub&gt;IHC&lt;/sub&gt; and Model&lt;sub&gt;Habitats+CF+IHC&lt;/sub&gt; (&lt;i&gt;p&lt;/i&gt; = 0.043).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The habit","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3711-3722"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045450","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":"Evaluation of the two-voltage method for parallel-plate ionization chambers irradiated with pulsed beams","authors":"José Paz-Martín,&nbsp;Andreas Schüller,&nbsp;Alexandra Bourgouin,&nbsp;Araceli Gago-Arias,&nbsp;Diego M. González-Castaño,&nbsp;Nicolás Gómez-Fernández,&nbsp;Juan Pardo-Montero,&nbsp;Faustino Gómez","doi":"10.1002/mp.17814","DOIUrl":"10.1002/mp.17814","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;Air-vented ionization chambers exposed to clinical radiation beams may suffer from recombination during the drift of the charge carriers towards the electrodes. Thus, dosimetry protocols recommend the use of a correction factor, usually denominated saturation factor (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mi&gt;sat&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$k_{rm sat}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), to correct the ionization chamber readout for the incomplete collection of charge. The two-voltage method (TVM) is the recommended methodology for the calculation of the saturation factor, however, it is based on the early Boag model, which only takes into account the presence of positive and negative ions in the ionization chamber and does not account for the electric field screening or the free electron contribution to the signal.&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;To evaluate the impact of a more realistic approach to the saturation problem that accounts for the free electron fraction.&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;The saturation factor of four ionization chambers (two Advanced Markus and two PPC05) was experimentally determined in the ultra-high dose per pulse reference beam of the German National Metrology Institute (Physikalisch-Technische Bundesanstalt [PTB]) for voltages ranging from 50 to 400 V and pulse durations between 0.5 and 2.9 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;μ&lt;/mi&gt;\u0000 &lt;mi&gt;s&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$umu{rm s}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. Several analytical models and a recently developed numerical model are used to calculate the saturation factor as a function of the dose per pulse and compare it to the obtained experimental data. Parameterizations of the saturation factor against the ratio of charges at different voltages are given for parallel plate ionization chamber with a distance between electrodes of 0.6 and 1 mm in pulsed beams for different pulse durations.&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;The saturation factors calculated using the different Boag analytical models do not agree neither with each other nor with the numerical simulation even at the lowest dose per pulse of the investigated range (&lt;span&gt;&lt;/span&gt;&lt;ma","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4894-4909"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059044","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":"Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images","authors":"Diya Sun,&nbsp;Yuru Pei,&nbsp;Liyi Ying,&nbsp;Tianbing Wang","doi":"10.1002/mp.17811","DOIUrl":"10.1002/mp.17811","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;Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemorrhages, using only healthy training data. Recent advancements in generative models have achieved success in unsupervised anomaly detection by transforming abnormal patterns into normal counterparts. However, current mask-free image generators often fail to maintain semantic consistency of anatomical structures during the restoration process. This limitation negatively impacts residual-based anomaly detection, particularly in cases where structural deformations occur due to the mass effect of TBI 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;This study aims to develop a semantic-consistent, unsupervised TBI lesion detection and segmentation method that minimizes false positives by preserving normal tissue consistency during the image generation process while addressing mass effect-related tissue deformations.&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;We propose the semantic-consistent diffusion model (SCDM) for unsupervised TBI lesion detection, focusing on the localization and segmentation of various lesion types from noncontrast CT scans of TBI patients. Leveraging the high-quality image generation capabilities of unconditioned diffusion models (DM), we introduce a normal tissue retainment (NTR) regularization to ensure that normal tissues remain unaltered throughout the iterative denoising process. Furthermore, we address normal tissue compression and deformation caused by the mass effect of TBI lesions through diffeomorphic registration, reducing erroneous activations in residual images and final lesion maps.&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;Extensive experiments were conducted on three publicly available brain lesion datasets and one internal dataset. These datasets comprised 75, 51, 92, and 56 CT scans, respectively. Thirty seven CT scans without TBI lesions were used for training and validation, while the remaining scans were used for testing. The proposed method achieved average DSC of 0.56, 0.51, 0.47, and 0.52 and AUPRC of 0.57, 0.48, 0.53, and 0.50 on the BCIHM, BHSD, Seg-CQ500, and internal datasets, respectively, surpassing state-of-the-art unsupervised methods for TBI lesion detection and segmentation. An ablation study validated the effectiveness of the proposed NTR regularization and diffeomorphic registration-based mass effect simulation.&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 results suggest that the proposed SCDM enabl","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4498-4512"},"PeriodicalIF":3.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030130","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":"Cover","authors":"","doi":"10.1002/mp.17204","DOIUrl":"https://doi.org/10.1002/mp.17204","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"C1"},"PeriodicalIF":3.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784282","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":"List of Advertisers","authors":"","doi":"10.1002/mp.17205","DOIUrl":"https://doi.org/10.1002/mp.17205","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2697"},"PeriodicalIF":3.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784305","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":"An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating","authors":"Zihan Sun,&nbsp;Jiazhou Wang,&nbsp;Weigang Hu,&nbsp;Yongheng Yan,&nbsp;Yuanhua Chen,&nbsp;Guorong Yao,&nbsp;Zhongjie Lu,&nbsp;Senxiang Yan","doi":"10.1002/mp.17795","DOIUrl":"10.1002/mp.17795","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;Previous knowledge-based planning studies have demonstrated the feasibility of predicting three-dimensional photon dose distributions and subsequently generating treatment plans. The steepness of dose fall-off represents a critical metric for clinical plan evaluation; however, dose fall-off similarity is frequently overlooked in dose prediction tasks. Our study introduces a novel automatic treatment planning methodology that specifically focuses on dose fall-off reconstruction for nasopharyngeal carcinoma (NPC).&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;Our study aims to establish an innovative methodology for automatic treatment plan generation that leverages dose fall-off information derived from deep learning-predicted dose distributions. Additionally, we propose and validate a comprehensive multicriteria rating strategy for dose prediction.&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;We incorporated 120 nasopharyngeal cancer cases in this study, distributing them into training (&lt;i&gt;n&lt;/i&gt; = 90), validation (&lt;i&gt;n&lt;/i&gt; = 10), and testing (&lt;i&gt;n&lt;/i&gt; = 20) cohorts. Three distinct dose prediction models were trained: U-Net, DoseNet, and Transformer. To determine the optimal dose prediction model, we developed a comprehensive multicriteria rating strategy that integrates mean absolute error, dose-volume histogram analysis, and isodose dice similarity coefficients. Based on these predictions, we implemented two automatic planning approaches: (1) IsoPlans, which extracts isodose lines from the predicted dose distribution to generate radiotherapy contours as optimization objectives and (2) DVH-IsoPlans, which enhances the first strategy by incorporating additional dose-volume constraints to further optimize treatment planning parameters.&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;The multicriteria scores for the three dose prediction models (U-Net, DoseNet, and Transformer) were 0.85, 0.84, and 0.82, respectively. The dose prediction model achieved a minimum mean absolute error of 2.71 Gy. In our clinical validation, 4 of the 20 generated IsoPlans failed to meet clinical requirements, whereas all 20 DVH-IsoPlans successfully satisfied clinical requirements. The mean plan optimization time for the 20 test cases was significantly reduced from 870 to 560 s for IsoPlans and to 470 s for DVH-IsoPlans, representing a substantial reduction of 37.5% and 50.5%, respectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4953-4970"},"PeriodicalIF":3.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782199","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":"Out-of-field neutron radiation from clinical proton, helium, carbon, and oxygen ion beams","authors":"Matteo Bolzonella,&nbsp;Marco Caresana,&nbsp;Andrea Cirillo,&nbsp;Josep M. Martí-Climent,&nbsp;Evangelina Martínez-Francés,&nbsp;Christina Mooshammer,&nbsp;Stefan Schmidt,&nbsp;Stephan Brons,&nbsp;Marco Silari,&nbsp;Christina Stengl,&nbsp;Liliana Stolarczyk,&nbsp;José Vedelago","doi":"10.1002/mp.17797","DOIUrl":"10.1002/mp.17797","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In hadron therapy, out-of-field doses, which may in the long-term cause secondary cancers, are mostly due to neutrons. Very recently, ⁴He and ¹⁶O beams have been added to protons and ¹²C ions for clinical therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The focus of this article is to compare secondary neutron doses produced by clinical protons, ⁴He, ¹²C, and ¹⁶O ion beams.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Ambient dose equivalent, <i>H</i>*(10), measurements were performed, with five types of rem counters, of the neutron field produced by the four primary ions impinging on a water phantom. This experiment was performed at the Heidelberg Ion Beam Therapy Center (HIT) in the framework of the activities of the European Radiation Dosimetry Group (EURADOS). The experimental data are normalized to both unit primary particle and target dose, and are further compared to Monte Carlo (MC) simulations performed with the FLUKA and MCNP codes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The intensity of the neutron field increases with ion mass, and the trend is more significant in the forward direction. The minimum <i>H</i>*(10) for all ions, 5µSv/Gy to 10µSv/Gy, was measured in the transverse and backward directions, whereas the maximum measured value was about 1.3 mSv/Gy for primary ¹⁶O ions in the forward direction. Additional MC simulations are presented for a more detailed analysis of the rem counters’ response in the presence of heavy charged fragments. In the downstream direction, for ¹²C and ¹⁶O ions, approximately only 30% of the instruments’ counts are due to neutrons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The four extended-range instruments provide reliable and consistent results, whereas the conventional rem counter underestimates <i>H</i>*(10) in a neutron field with a large high-energy component. FLUKA and MCNP provide consistent predictions, within a factor of 1.6 for the downstream position and lower differences in the other cases, and are in agreement with the experimental data. It was found that under certain conditions neutrons do not represent the only secondary radiation field to be monitored; the presence of charged particles affects the performance of moderator-type neutron detectors.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4924-4940"},"PeriodicalIF":3.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782268","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}