Medical physics最新文献

{"title":"Automatic joint inflammation estimation based on regression neural networks","authors":"Yanli Li,&nbsp;Dennis A. Ton,&nbsp;Denis P. Shamonin,&nbsp;Monique Reijnierse,&nbsp;Annette H. M. van der Helm-van Mil,&nbsp;Berend C. Stoel","doi":"10.1002/mp.70010","DOIUrl":"10.1002/mp.70010","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Quantitative assessment of inflammation from hand and forefoot MRI scans is crucial for evaluating the severity, progression, and treatment response in inflammatory disease like rheumatoid arthritis (RA). Traditionally, this relies on visual evaluation of signs like bone marrow edema (BME), tenosynovitis, and synovitis, which is time-consuming, subjective, and prone to inherent inter/intra-reader variability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims at an automatic DL-based MRI analysis of inflammatory signs in RA system for inflammation assessment to facilitate related diagnoses and studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed an <b>A</b>utomatic <b>D</b>L-based <b>M</b>RI analysis of <b>I</b>nflammatory signs in <b>RA</b> (ADMIRA) system for inflammation assessment, using pre- and post-processing alongside DL models to estimate inflammation scores from fat saturated, contrast-enhanced T1-weighted MRI scans of 2254 subjects across four study populations. These MRI scans include three different anatomical sites, wrist, metacarpophalangeal (MCP) and metatarsophalangeal (MTP) joints, as the objects for inflammation assessment. The scans were divided into training, monitoring, testing and validation sets to ensure robust performance evaluation, using Pearson's correlation coefficients and Intra-class correlation coefficients. A revised class activation mapping (CAM) algorithm was used to validate the DL model's reliability, illustrating its inference process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The system achieved mean R/ICCs of nearly 0.9 for synovitis and tenosynovitis on test sets and 0.8 on the validation set, with slightly lower scores for BME (0.8 and 0.7, respectively). This system presents a performance close to human experts on the same datasets. Meanwhile, the visualization results indicate the DL models have a inference process consistent with expert knowledge.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Results show that ADMIRA provides accurate, expert-level inflammation estimation, particularly for synovitis and tenosynovitis, offering a fast, reliable alternative to manual methods for RA monitoring and analysis. We expect that this automatic method could help to reduce labor costs and improve the efficiency of diagnosis in the future.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110715","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":"A multi-arc and constant-energy-per-arc treatment planning method for efficient proton arc therapy","authors":"Jiangjun Ma,&nbsp;Ya-Nan Zhu,&nbsp;Yuting Lin,&nbsp;Min Tang,&nbsp;Hao Gao","doi":"10.1002/mp.18089","DOIUrl":"10.1002/mp.18089","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;Proton arc therapy is an emerging proton modality with enhanced dose and linear energy transfer (LET) conformality to treatment targets. However, its delivery efficiency can be negatively impacted by frequent energy changes.&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 work will develop a novel proton arc treatment planning method that has constant energy and thus does not require energy changes per arc, that is, a multi-arc and constant-energy-per-arc approach, for achieving efficient delivery of proton arc while optimizing its plan quality.&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 proposed method is based on a novel block orthogonal matching pursuit (BOMP) optimization algorithm. The optimization process involves an iterative approach that alternates between BOMP-based energy selection and the optimization of spot weights on the selected arcs subject to the minimum-monitor-unit deliverability constraint. For method validation, the proposed BOMP is compared with (1) the heuristic-search (HS) method, in which each arc is optimized individually and then arcs with the best plan quality are jointly selected to optimize the final plan; (2) the conventional (CONV) method, which has a variable-energy arc, serving as the ground truth for plan quality; (3) the energy layer optimization (ELO) method, which minimizes the number of energy jumps for CONV.&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;BOMP had better plan quality than HS for multi-arc planning (e.g., 5-arc) in this work, which validates the efficacy of the BOMP method. Compared to IMPT, BOMP5 offers superior plan quality while also being comparable in total plan delivery time. For example, in a liver case, BOMP5 increased the conformity index (CI) from 0.80 to 0.86, reduced the max target dose from 130.1% to 116.4%, and also decreased the total plan delivery time from 354.4 to 259.9 s. Compared to CONV and ELO, BOMP substantially reduced energy layer switching time without sacrificing the plan quality. For example, compared to CONV, BOMP reduced the optimization objective value from 0.358–0.231, reduced max target dose from 116.3% to 110.8%, reduced the ≥10 Gy volume of brainstem from 0.84cc to 0.43cc and improved the CI from 0.83 to 0.84 for a head-and-neck case. Compared to CONV, the energy layer switching times for the head-and-neck case, liver case, and lung case have been significantly reduced from 759.4, 1918.6, and 916.2 to 2.8 s, respectively. And the total plan delivery time significantly reduced from 790.7 to 149.2 s, 2184.9 to 259.9 s, and 1017.6 to 125.0 s, respectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110718","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":"Feasibility study of using CNN-GRU-Dense model for real-time liver tumor tracking during radiotherapy","authors":"Payam Samadi Miandoab,&nbsp;Yaoying Liu,&nbsp;Xuying Shang,&nbsp;Tie Lv,&nbsp;Hui jun Xu,&nbsp;Gaolong Zhang,&nbsp;Shouping Xu","doi":"10.1002/mp.70014","DOIUrl":"10.1002/mp.70014","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;Real-time tumor tracking helps overcome challenges in delivering accurate radiotherapy. Commercial tracking devices use a hybrid external–internal correlation model (ECM) that combines intermittent X-ray imaging of the tumor's internal position with continuous monitoring of external respiratory motion. This approach improves tracking accuracy and treatment effectiveness.&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 simulated using a deep learning model (CNN-GRU-Dense model) to track liver tumors in real-time during treatment—without needing ongoing updates. The model's accuracy was tested against several well-known methods, including the hybrid correlation model used in the CyberKnife system, the NG-RC model, and the augmented linear model.&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 CNN-GRU-Dense model comprises convolutional, Gated Recurrent Units (GRU), and dense layers to estimate tumor position in various directions. Initially, input signals are processed through a 1D convolutional layer that employs 64 filters with a kernel size of 3 and ReLU activation to extract spatial features. Next, the extracted features are processed by two stacked GRU layers, each containing 256 units with ReLU activation, enabling the model to capture temporal dependencies. After the GRU layers, the data undergoes refinement through two dense (fully connected) layers, each with 64 units and ReLU activation, ensuring enhanced feature extraction. Finally, the output is passed through a single-unit output layer with linear activation, providing the estimated tumor position. For training the CNN-GRU-Dense model, 26 min of motion patterns (a patient-specific data) are utilized. The proposed model underwent hyperparameter optimization using the RandomSearch approach. This method explored a broad search space, which included the number of filters and kernel size in the 1D Convolutional layer, the number of GRU units, the number of fully connected dense layers, the learning rate, and the loss function. Using a learning rate 0.001, the model was optimized with the Adam optimizer and trained with the mean squared error (MSE) loss function. The training was conducted for 30 epochs with a batch size of 300, aiming to strike a balance between speed and stability during the learning process. Finally, the trained CNN-GRU-Dense model was tested with new external motion data to estimate tumor positions. The model parameters remain unchanged throughout the treatment, requiring no updates. Fifty-seven motion trace datasets from the CyberKnife system were used to evaluate the CNN-GRU-Dense model performance. These traces were grouped into three liver regions: central, lower, and upper.&lt;/p&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102278","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":"Modular breast and tumor perfusion phantoms for 4D dynamic contrast-enhanced dedicated breast CT","authors":"Liselot C. Goris,&nbsp;Sanne Gouma,&nbsp;Juan J. Pautasso,&nbsp;Koen Michielsen,&nbsp;Ioannis Sechopoulos","doi":"10.1002/mp.70004","DOIUrl":"10.1002/mp.70004","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;Tumor heterogeneity presents significant challenges in breast cancer diagnosis and treatment. Four-dimensional dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT) is a novel imaging technique designed to capture contrast agent kinetics with high spatial and temporal resolution, enabling detailed assessment of tumor perfusion and heterogeneity.&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 develop modular breast tumor phantoms capable of simulating a range of physiologically relevant perfusion patterns and structural heterogeneities for validation of 4D DCE-bCT.&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;Tumor phantoms (1.5 cm diameter) were 3D-printed using clear resin in several designs: small-channel phantoms (0.8 and 1.0 mm diameter), a leaking vessel model with permeable outer walls, gyroid structures (1.3 and 1.5 mm pores) mimicking microvascularization, and a dual-input/output model to replicate heterogeneous perfusion. The phantoms were integrated into a programmable flow system, enabling iodinated contrast (up to 5 mg I/mL) delivery with custom profiles: full wash-in/wash-out, persistent, plateau, and partial wash-out. 4D DCE-bCT acquisitions with a 65 kV + 0.25 mm Cu filter involved 1 pre-contrast scan (360 pulses over a 10-second revolution at 80 mA) followed by three post-contrast phases (400 pulses over 10 revolutions at 32 mA). Images were reconstructed using 40 projections at 5-second intervals using prior image constrained compressed sensing (PICCS). Time–intensity curves (TICs) were analyzed in volumes of interest in various sections of the tumor phantoms. A gamma variate function was fitted to each TIC, and the corresponding fit parameters and coefficients of determination (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;) were extracted.&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;Dynamic imaging demonstrated successful capture of expected contrast kinetics. Larger channels (1.0 mm) produced 2.5 times greater enhancement compared to smaller ones (0.8 mm). The &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; values for the 0.8 mm and leaking channel were lower, 0.54 and 0.67 versus 0.85 in the 1 mm channel, due to a higher noise level in the signal. The leaking vessel phantom exhibited delayed wash-out by ∼30 s. Distinct flow patterns were evident in the dual-input/output model. The gamma variate parameters showed the expected trends due to the programmed flow patterns.&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 developed ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093340","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":"Lesion-guided selective multi-modal integration for prostate cancer segmentation and PI-RADS grading in MP-MRI","authors":"Menglin Wu,&nbsp;Fan Li,&nbsp;Yuhui Tao,&nbsp;Yuhan Zhang,&nbsp;Shanshan Wang,&nbsp;Pablo D. Burstein,&nbsp;Xuetao Mu,&nbsp;Jie Zhu","doi":"10.1002/mp.70019","DOIUrl":"10.1002/mp.70019","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;Prostate cancer (PCa) presents a significant global health challenge affecting men. Accurate segmentation and grading of PCa lesions in multiparametric Magnetic Resonance Imaging (mp-MRI) are essential for effective diagnosis and treatment planning.&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 aimed to develop and validate an automated model for PCa lesion segmentation and Prostate Imaging Reporting and Data System (PI-RADS) grading in mp-MRI.&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 lesion's perceived characteristics are strongly related to both imaging modalities and lesion locations. Therefore, we propose a Lesion-guided Selective Multi-modal Integration (LeSMI) module. This module incorporates two advanced mechanisms—Dynamic Modality Weighting (DMW) and Localized Lesion Attention (LLA)—to dynamically integrate crucial information across and within imaging modalities. Specifically, DMW operates on the mp-MRI inputs (T2-weighted (T2w) images, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps) to dynamically assign weights to each modality, thereby integrating complementary information and enhancing feature identification across different contexts. LLA, on the other hand, maintains spatial structure information within each modality for precise lesion localization. Inspired by clinical workflows, our framework is employed through a two-stage Prostate Cancer Segmentation and Grading (PCaSG) strategy, leveraging knowledge from segmentation to improve PI-RADS grading performance. We validated our method using two publicly available datasets, namely, Prostate158 and PI-CAI Challenge, to assess its advantages over other methods. For the Prostate158 dataset, we used the officially reported partition with 119 cases for training, 20 for validation, and 19 for testing. In contrast, the PI-CAI Challenge dataset, which lacks predefined splits, was randomly divided into 180 for training, 20 for validation, and 20 for testing. In addition to these dataset partitions, 5-fold cross-validation was conducted on both the Prostate158 and PI-CAI Challenge datasets to provide a more robust and comprehensive statistical evaluation of the model's performance.&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;Evaluated on the Prostate158 and PI-CAI Challenge datasets, our method demonstrated superior performance, achieving a Dice Similarity Coefficient (DSC) of 51.30% and a lesion-level quadratic-weighted kappa score (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mi&gt;W&lt;/mi&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093333","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":"Partitioning of multiple brain metastases improves dose gradients in single-isocenter radiosurgery","authors":"Johan Sundström,&nbsp;Anton Finnson,&nbsp;Elin Hynning,&nbsp;Geert De Kerf,&nbsp;Albin Fredriksson","doi":"10.1002/mp.18117","DOIUrl":"10.1002/mp.18117","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;A growing number of cancer patients with brain metastases can benefit from stereotactic radiosurgery (SRS) thanks to recent advances in systemic therapies which have led to improved survival. Meanwhile, selection criteria for SRS treatments are evolving to include patients with increasingly many metastases. With an increasing patient load, single-isocenter treatments on widely available C-arm linear accelerators are an attractive option. However, the planning of such treatments is challenging for multi-target cases due to the island blocking problem, which occurs when the multi-leaf collimator cannot conform to all targets simultaneously.&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 propose a multi-target partitioning algorithm that mitigates excessive exposure of normal tissue caused by the island blocking problem.&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 proposed algorithm considers an initial set of arc trajectories and divides (partitions) the set of targets per trajectory into smaller subsets to treat with separate back-and-forth arc passes, simultaneously optimizing both the target subsets and collimator angles to minimize island blocking. We incorporated this algorithm into a fully automated treatment planning script and evaluated it on 20 simulated patient cases, each with 10 brain metastases and 21 Gy prescriptions. For each case, the script generated a series of volumetric modulated arc therapy (VMAT) plans with increasingly many arcs along the three trajectories. Each such plan was compared to four baseline plans generated with alternative heuristics for distributing targets across arcs. We also evaluated the algorithm retrospectively on six clinical cases.&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;Partitioning significantly improved the gradient index (GI), global efficiency index (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;G&lt;/mi&gt;\u0000 &lt;mi&gt;η&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm G}eta$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and brain &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;V&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;12&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;Gy&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{V}_{12nobreakspace mathrm{Gy}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; compared to simultaneous treatment of all metastases. For exam","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093344","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":"Relative optimized linearization for radiochromic film dosimetry with non-uniformity correction","authors":"Nicholas G. Zacharopoulos,&nbsp;Piotr Pater","doi":"10.1002/mp.70012","DOIUrl":"10.1002/mp.70012","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;Traditional radiochromic film dosimetry requires batch-specific dose-response curve measurements, which are time-consuming and add complexity to clinical workflows. While relative dosimetry techniques have been proposed to streamline the process, they have neglected film non-uniformities, limiting their accuracy.&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 develop and validate a relative optimized linearization (ROL) method for radiochromic film dosimetry that eliminates the need for dose-response curve measurements while incorporating non-uniformity corrections for improved accuracy.&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 accuracy of the linearization method proposed by Devic et al. was first evaluated through simulations using EBT4 film dose-response data, with maximum dose values ranging from 1 to 10 Gy. Based on these results, the linearization was refined with an optimized power function to reduce errors across all dose ranges. The optimized linearization was then integrated into the multichannel dosimetry (MCD) framework of Micke et al. to correct for dose-independent variations, forming the ROL method. ROL was validated against MCD using measured film data from open field, wedge field, and volumetric modulated arc therapy (VMAT) plans. To assess robustness, the VMAT test case was further evaluated under induced positional and dose delivery errors. Sensitivity to treatment planning modeling errors was also examined.&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;Simulations showed that optimized linearization using ROL reduced average errors from up to 3% in the green channel and 2% in the blue channel to below 1% across all channels and dose ranges. ROL produced dose distributions comparable to MCD (within 1%), particularly in the open and VMAT fields. Only small regions in the wedge field, specifically in the toe region, exceeded 1%, but remained below 1.5%. Sensitivity tests confirmed ROL's robustness to spatial errors and to more subtle treatment planning variations in MLC modeling. Partial plan deliveries, which effectively scale the measured dose distribution, showed expected deviations from MCD. However, gamma analysis of the ROL-computed dose successfully detected the partial delivery error.&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 ROL method provides an efficient alternative to traditional film dosimetry by removing the need for time-consuming calibration curves whil","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093366","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":"Monte Carlo–calculated beam–quality and perturbation correction factors for helium–, carbon–, and neon–ion beams","authors":"Yuka Urago,&nbsp;Makoto Sakama,&nbsp;Dousatsu Sakata,&nbsp;Tetsurou Katayose,&nbsp;Weishan Chang","doi":"10.1002/mp.70024","DOIUrl":"10.1002/mp.70024","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A new technique, multi-ion radiotherapy, which uses He, O, and Ne ions in addition to C ions, has been proposed, though there is still limited information on the dosimetric quantities, such as the beam quality and perturbation correction factors, necessary to determine the absorbed dose-to-water for various ion species. In this study, beam-quality correction factors, &lt;i&gt;k&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt;, for He, C, and Ne ions in five types of ionization chambers were derived through Monte Carlo simulations. Additionally, perturbation effects attributed to the ionization chambers were analyzed in detail, and differences in these effects across chamber types and ions were clarified.&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 &lt;i&gt;k&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt; values for 150 MeV/u-He, 290 MeV/u-C, and 400 MeV/u-Ne ions were evaluated using the Geant4 Monte Carlo code. The &lt;i&gt;f&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt; (the product of the water-to-air stopping power ratio and the perturbation correction factor) was obtained using the absorbed doses to water and to air inside the active volume of the ionization chamber. The &lt;i&gt;k&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt; values were then calculated from the &lt;i&gt;f&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt; and the literature-extracted factors, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;f&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${f_{{Q_0}}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;i&gt;W&lt;/i&gt;&lt;sub&gt;air&lt;/sub&gt;. To evaluate individual perturbation effects, each chamber component (such as the central electrode, wall, and stem in the modeled chambers) was replaced by water in turn, and the dose was recalculated for each modified geometry. From the ratio of these doses, the contribution of each component to the perturbation effect was estimated.&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 derived &lt;i&gt;k&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt; values were consistent with the code of practice for dosimetry, the revised TRS-398, and varied slightly with the ion species, from He to Ne ions. Additionally, the &lt;i&gt;k&lt;/i&gt;&lt;sub&gt;Q&lt;/sub&gt; values increased by up to 1% compared with the current protocol values, particularly in plane-parallel chambers. Analysis of contributions to the perturbation factors revealed that the perturbation effect due to the cavity, &lt;i&gt;P&lt;/i&gt;&lt;sub&gt;cav&lt;/sub&gt;, is significantly greater for light ions than for protons and had the most substantial impact among the evaluated individual perturbation factors.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088760","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":"Single-time-point tumor dosimetry using population-based model selection and nonlinear mixed-effects modeling in [177Lu]Lu-PSMA-617 therapy","authors":"Deni Hardiansyah,&nbsp;Elham Yousefzadeh-Nowshahr,&nbsp;Indra Budiansah,&nbsp;Ursula Nemer,&nbsp;Ade Riana,&nbsp;Felix Kind,&nbsp;Ambros J. Beer,&nbsp;Philipp T. Meyer,&nbsp;Gerhard Glatting,&nbsp;Michael Mix","doi":"10.1002/mp.70000","DOIUrl":"10.1002/mp.70000","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;Molecular radiotherapy with [&lt;sup&gt;177&lt;/sup&gt;Lu]Lu-PSMA-617 is an effective treatment for metastatic castration-resistant prostate cancer. Accurate dosimetry is essential for maximizing therapeutic efficacy while minimizing toxicity. However, standard dosimetry requires multiple imaging sessions, posing logistical challenges. Single-time-point (STP) dosimetry offers a practical alternative but remains challenging for tumor kinetics due to high inter-patient variability. Nonlinear mixed-effects (NLME) modeling, combined with population-based model selection (PBMS), has demonstrated potential for improving STP dosimetry accuracy.&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 purpose of this study was to evaluate the accuracy of STP tumor dosimetry using SPECT/CT data, PBMS, and an NLME model in a large population with diverse biokinetic measurements for [&lt;sup&gt;177&lt;/sup&gt;Lu]Lu-PSMA-617 therapy.&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;Biokinetic data for [&lt;sup&gt;177&lt;/sup&gt;Lu]Lu-PSMA-617 in tumors were obtained from forty-nine patients with metastatic castration-resistant prostate cancer using SPECT/CT at time points (1.80 ± 0.80), (18.67 ± 0.90), (42.63 ± 1.03), (66.27 ± 0.96), and (159.02 ± 23.35) h after injection. Ten different functions, derived from various parameterizations of two- to four-exponential functions, were fitted to the data using the NLME framework. Each function's parameters were defined as a combination of fixed and random effects. A PBMS approach was employed, using goodness-of-fit tests and Akaike weights to identify the function best supported by the data. The selected function from the NLME fitting of all time points with the leave-one-out method was used to calculate the reference time-integrated activities per volume (TIAVs). The parameters from STP fitting were used to calculate the STP TIAVs. Additionally, STP dosimetry was performed using the Hänscheid method to calculate the TIAVs. Relative deviations (RDs) and root-mean-square errors (RMSEs) were used to analyse the accuracy of the calculated STP TIAVs and Hänscheid method TIAVs compared with the reference TIAVs.&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 function &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;f&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;4&lt;/mn&gt;\u0000 &lt;mi&gt;b&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088747","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":"List of Advertisers","authors":"","doi":"10.1002/mp.70032","DOIUrl":"10.1002/mp.70032","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101308","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}