Medical physics最新文献

{"title":"Acoustic signal-based precise positioning of Bragg peak for laser-accelerated monoenergetic proton pulses.","authors":"Zihao Zhang, Zhusong Mei, Qihang Han, Shuang Li, Ke Chen, Guangjie Zhang, Tao Han, Zhengxuan Cao, Mingjian Wu, Jungao Zhu, Dongyu Li, Hongxin Zhao, Yibao Zhang, Chen Lin, Kun Zhu, Xueqing Yan, Xiaoping Ouyang, Changhui Li, Wenjun Ma","doi":"10.1002/mp.17926","DOIUrl":"https://doi.org/10.1002/mp.17926","url":null,"abstract":"<p><strong>Background: </strong>With the advancement of ultra-short pulse technology and the rapid progress of FLASH radiotherapy, it is clinically desirable and technically possible to utilize the radiation acoustic effect of radiotherapy pulses for noninvasive real-time in vivo dose monitoring.</p><p><strong>Purpose: </strong>As a crucial foundation of in vivo dose monitoring using laser-accelerated proton acoustics, this study focuses on measuring, analyzing, and processing the acoustic signals to precisely position the Bragg peak of laser-accelerated monoenergetic proton pulses.</p><p><strong>Materials and methods: </strong>Nanosecond-scale high-energy broadband proton bunches were produced from the interaction of ultra-intense femtosecond laser pulses with thin film targets. After energy selection and focusing through an electromagnetic beamline, approximately 10⁷ quasi-monoenergetic protons per shot were delivered into a water gel or tank. Ultrasonic transducers with different center frequencies detected acoustic signals across various frequency bands. Time and frequency domain analyses were conducted to achieve precise positioning of Bragg peaks.</p><p><strong>Results: </strong>This study successfully achieved measurement of acoustic signals of laser-driven ultra-short monoenergetic protons for the first time. Subsequent analysis and processing of signals enabled the precise positioning of the Bragg peak with a deviation of 45 µm, demonstrating the potential of this method for dose monitoring.</p><p><strong>Conclusions: </strong>Our findings indicate that this method can be applied to single-shot in vivo dose monitoring in radiotherapy equipment based on laser proton accelerators. It can potentially promote the precise and effective dose delivery of radiotherapy.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dose to circulating blood in intensity-modulated total body irradiation, total marrow irradiation, and total marrow and lymphoid irradiation.","authors":"Bingqi Guo, Sheen Cherian, Erin S Murphy, Craig S Sauter, Ronald M Sobecks, Seth Rotz, Rabi Hanna, Jacob G Scott, Ping Xia","doi":"10.1002/mp.17913","DOIUrl":"https://doi.org/10.1002/mp.17913","url":null,"abstract":"<p><strong>Background: </strong>Multi-isocentric intensity-modulated (IM) total body irradiation (TBI), total marrow irradiation (TMI), and total marrow and lymphoid irradiation (TMLI) are gaining popularity. A question arises on the impact of the interplay between blood circulation and dynamic delivery on blood dose.</p><p><strong>Purpose: </strong>This study answers the question by introducing a new whole-body blood circulation modeling technique.</p><p><strong>Methods: </strong>A whole-body CT with intravenous contrast was used to develop the blood circulation model. Fifteen organs and tissues, heart chambers, and great vessels were segmented using a deep-learning-based auto-contouring software. The main blood vessels were segmented using an in-house algorithm. Blood density, velocity, time-to-heart, and perfusion distributions were derived for systole, diastole, and portal circulations and used to simulate trajectories of blood particles during delivery. With the same prescription of 12 Gy in 8 fractions, doses to circulating blood were calculated for three plans: (1) an IM-TBI plan prescribing uniform dose to the whole body while reducing lung and kidney doses; (2) a TMI plan treating all bones; and (3) a TMLI plan treating all bones, major lymph nodes, and spleen; TMI and TMLI plans were optimized to reduce doses to non-target tissue.</p><p><strong>Results: </strong>Circulating blood received 1.57 ± 0.43 Gy, 1.04 ± 0.32 Gy, and 1.09 ± 0.32 Gy in one fraction and 12.60 ± 1.21 Gy, 8.34 ± 0.88 Gy, and 8.71 ± 0.92 Gy in 8 fractions in IM-TBI, TMI, and TMLI, respectively. The interplay effect of blood motion with IM delivery did not change the mean dose, but changed the dose heterogeneity of the circulating blood. Fractionation reduced the blood dose heterogeneity.</p><p><strong>Conclusions: </strong>A novel whole-body blood circulating model was developed based on patient-specific anatomy and realistic blood dynamics, concentration, and perfusion. Using the blood circulation model, we developed a dosimetry tool for circulating blood in IM-TBI, TMI, and TMLI.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient operator-splitting minimax algorithm for robust optimization.","authors":"Jiulong Liu, Ya-Nan Zhu, Xiaoqun Zhang, Hao Gao","doi":"10.1002/mp.17929","DOIUrl":"https://doi.org/10.1002/mp.17929","url":null,"abstract":"<p><strong>Background: </strong>The treatment uncertainties such as patient positioning can significantly affect the accuracy of proton radiation therapy (RT). Robust optimization can account for these uncertainties during treatment planning, for which the minimax approach optimizes the worst-case plan quality.</p><p><strong>Purpose: </strong>This work will develop an efficient minimax robust optimization algorithm for improving plan quality and computational efficiency.</p><p><strong>Methods: </strong>The proposed method reformulates the minimax problem so that it can be conveniently solved by the first-order operator-splitting algorithm (OS). That is, the reformulated problem is split into several subproblems, which either admit a closed-form solution or can be efficiently solved as a linear system.</p><p><strong>Results: </strong>The proposed method OS was demonstrated with improved plan quality, robustness, and computational efficiency, compared to robust optimization via stochastic programming (SP) and current minimax robust method via minimax stochastic programming (MSP). For example, in a prostate case, compared to MSP and SP, OS decreased the max target dose from 140% and 121% to 118%, and the mean femoral head dose from 28.6% and 26.3% to 24.8%. In terms of robustness, OS reduced the robustness variance (RV₁₂₀) of the target from 56.07 and 0.30 to 0.04. Compared to MSP, OS decreased the computational time from 16.4 min to 1.7 min.</p><p><strong>Conclusions: </strong>A novel operator-splitting minimax robust optimization is proposed with improved plan quality and computational efficiency, compared to conventional minimax robust optimization method MSP and probabilistic robust optimization method SP.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion semantic segmentation model: A generative model for medical image segmentation based on joint distribution.","authors":"Tiange Liu, Jinze Li, Drew A Torigian, Yubing Tong, Qibing Xiong, Kaige Zhang, Jayaram K Udupa","doi":"10.1002/mp.17928","DOIUrl":"https://doi.org/10.1002/mp.17928","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The mainstream semantic segmentation schemes in medical image segmentation are essentially discriminative paradigms based on conditional distributions &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt; &lt;mi&gt;l&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mo&gt;|&lt;/mo&gt; &lt;mi&gt;f&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;u&lt;/mi&gt; &lt;mi&gt;r&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$p( {class|feature} )$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; . Although efficient and straightforward, this prevalent paradigm focuses solely on extracting image features while ignoring the underlying data distribution &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;u&lt;/mi&gt; &lt;mi&gt;r&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mo&gt;|&lt;/mo&gt; &lt;mi&gt;c&lt;/mi&gt; &lt;mi&gt;l&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$p( {feature|class} )$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; . Therefore, the learned feature space exhibits inherent instability, which directly affects the precision of the model in delineating anatomical boundaries.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This paper reformulates the semantic segmentation task as a distribution alignment problem for medical image segmentation, aiming to minimize the gap between model predictions and ground truth labels by modeling the joint distribution of the data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We propose a novel segmentation architecture based on joint distribution, called Denoising Semantic Segmentation Model (DSSM). We propose learning classification decision boundaries in pixel feature space and modeling joint distributions in latent feature space. Specifically, DSSM optimizes probability maps based on pixel feature classification through Bayesian posterior probabilities. To this end, we design a Feature Fusion Module (FFM) to guide the generative module in inference and provide label features for the semantic module. Furthermore, we introduce a stable Markov inference process to reduce inference offset. Finally, the joint distribution-based model is end-to-end trained in a discriminative manner, that is, maximizing &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt; &lt;mi&gt;l&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mo&gt;|&lt;/mo&gt; &lt;mi&gt;f&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;u&lt;/mi&gt; &lt;mi&gt;r&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$p( {class|feature} )$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , which endows DSSM with the strengths of both generative and discriminative models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The image datasets utilized in this study are from different modalities, including MRI scans, x-ray images, and skin lesion photographic images, demonstrating superior performance compared to state-of-the-art (SOTA) discriminative models. Specifically, DSSM achieved a Dice coefficient of 0.8871 in MSD cardiac MRI segmentation, 0.9451 in ACDC left ventricular MRI segmentation, and 0.9647 in x-ray image segmentation. DSSM also reached 0.8731 Dice in prostate MRI seg","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of a shielded beam current transformer for ultra-high dose rate (FLASH) electron beam monitoring and dose reporting.","authors":"Thibault Bernelin, Bryan Muir, James Renaud, Karim Zerouali, Dominique Guillet, Louis Archambault, Arthur Lalonde","doi":"10.1002/mp.17927","DOIUrl":"https://doi.org/10.1002/mp.17927","url":null,"abstract":"<p><strong>Background: </strong>Real-time beam monitoring and accurate dose reporting is challenging in ultra-high dose rate (UHDR) electron beams. Although beam current transformers (BCTs) can effectively track parameters such as pulse width (PW) and repetition frequency for UHDR electron beams, recent work has highlighted their sensitivity to electric fields induced by transient charge buildup in irradiated media under UHDR conditions.</p><p><strong>Purpose: </strong>This study evaluates the performance of a novel electrostatically shielded BCT for real-time, high-accuracy dose monitoring in UHDR electron beams.</p><p><strong>Methods: </strong>Irradiations were conducted using the Mobetron linear accelerator configured for UHDR electron beams with energies of 6 and 9 MeV. A shielded BCT was implemented to monitor beam delivery, with dose calibration established using alanine dosimeters in solid water phantoms. Dose stability was assessed over short (7-day) and long (16-week) periods. The BCT's response to variations in PW, pulse number, and pulse repetition frequency was also evaluated to determine its robustness across beam configurations.</p><p><strong>Results: </strong>The BCT showed high reproducibility and accuracy, with standard deviations of the difference between BCT-predicted and alanine-measured doses within 0.21% over short-term measurements and 0.57% over long-term measurements, even when subject to large (10%) machine output adjustments. When varying beam parameters, the BCT maintained accurate dose prediction within 1.0% and 1.4% of alanine measurements for 6 and 9 MeV, respectively, with high linearity ( <math> <semantics> <mrow><msup><mi>R</mi> <mn>2</mn></msup> <mo>≥</mo></mrow> <annotation>${rm R}^2ge$</annotation></semantics> </math> 0.9997) across total doses.</p><p><strong>Conclusion: </strong>Shielded BCTs provide a stable and accurate solution for real-time dose monitoring in FLASH radiotherapy, demonstrating robustness against output fluctuations and beam parameter variations. While further calibration standardization is required, this study supports the feasibility of using shielded BCTs for reliable UHDR dose monitoring, facilitating safe and precise implementation of FLASH radiotherapy in preclinical and clinical settings.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Matrix completion-informed deep unfolded equilibrium models for self-supervised <ns0:math><ns0:semantics><ns0:mi>k</ns0:mi> <ns0:annotation>$k$</ns0:annotation></ns0:semantics> </ns0:math> -space interpolation in MRI.","authors":"Chen Luo, Huayu Wang, Yuanyuan Liu, Taofeng Xie, Guoqing Chen, Qiyu Jin, Dong Liang, Zhuo-Xu Cui","doi":"10.1002/mp.17924","DOIUrl":"https://doi.org/10.1002/mp.17924","url":null,"abstract":"<p><strong>Background: </strong>Self-supervised methods for magnetic resonance imaging (MRI) reconstruction have garnered significant interest due to their ability to address the challenges of slow data acquisition and scarcity of fully sampled labels. Current regularization-based self-supervised techniques merge the theoretical foundations of regularization with the representational strengths of deep learning and enable effective reconstruction under higher acceleration rates, yet often fall short in interpretability, leaving their theoretical underpinnings lacking.</p><p><strong>Purpose: </strong>In this paper, we introduce a novel self-supervised approach that provides stringent theoretical guarantees and interpretable networks while circumventing the need for fully sampled labels.</p><p><strong>Methods: </strong>Our method exploits the intrinsic relationship between convolutional neural networks and the null space within structural low-rank models, effectively integrating network parameters into an iterative reconstruction process. Our network learns gradient descent steps of the projected gradient descent algorithm without changing its convergence property, which implements a fully interpretable unfolded model. We design a non-expansive mapping for the network architecture, ensuring convergence to a fixed point. This well-defined framework enables complete reconstruction of missing <math><semantics><mi>k</mi> <annotation>$k$</annotation></semantics> </math> -space data grounded in matrix completion theory, independent of fully sampled labels.</p><p><strong>Results: </strong>Qualitative and quantitative experimental results on multi-coil MRI reconstruction demonstrate the efficacy of our self-supervised approach, showing marked improvements over existing self-supervised and traditional regularization methods, achieving results comparable to supervised learning in selected scenarios. Our method surpasses existing self-supervised approaches in reconstruction quality and also delivers competitive performance under supervised settings.</p><p><strong>Conclusions: </strong>This work not only advances the state-of-the-art in MRI reconstruction but also enhances interpretability in deep learning applications for medical imaging.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based cone-beam CT motion compensation with single-view temporal resolution.","authors":"Joscha Maier, Stefan Sawall, Marcel Arheit, Pascal Paysan, Marc Kachelrieß","doi":"10.1002/mp.17911","DOIUrl":"https://doi.org/10.1002/mp.17911","url":null,"abstract":"<p><strong>Background: </strong>Cone-beam CT (CBCT) scans that are affected by motion often require motion compensation to reduce artifacts or to reconstruct 4D (3D+time) representations of the patient. To do so, most existing strategies rely on some sort of gating strategy that sorts the acquired projections into motion bins. Subsequently, these bins can be reconstructed individually before further post-processing may be applied to improve image quality. While this concept is useful for periodic motion patterns, it fails in case of non-periodic motion as observed, for example, in irregularly breathing patients.</p><p><strong>Purpose: </strong>To address this issue and to increase temporal resolution, we propose the deep single angle-based motion compensation (SAMoCo).</p><p><strong>Methods: </strong>To avoid gating, and therefore its downsides, the deep SAMoCo trains a U-net-like network to predict displacement vector fields (DVFs) representing the motion that occurred between any two given time points of the scan. To do so, 4D clinical CT scans are used to simulate 4D CBCT scans as well as the corresponding ground truth DVFs that map between the different motion states of the scan. The network is then trained to predict these DVFs as a function of the respective projection views and an initial 3D reconstruction. Once the network is trained, an arbitrary motion state corresponding to a certain projection view of the scan can be recovered by estimating DVFs from any other state or view and by considering them during reconstruction.</p><p><strong>Results: </strong>Applied to 4D CBCT simulations of breathing patients, the deep SAMoCo provides high-quality reconstructions for periodic and non-periodic motion. Here, the deviations with respect to the ground truth are less than 27 HU on average, while respiratory motion, or the diaphragm position, can be resolved with an accuracy of about 0.75 mm. Similar results were obtained for real measurements where a high correlation with external motion monitoring signals could be observed, even in patients with highly irregular respiration.</p><p><strong>Conclusions: </strong>The ability to estimate DVFs as a function of two arbitrary projection views and an initial 3D reconstruction makes deep SAMoCo applicable to arbitrary motion patterns with single-view temporal resolution. Therefore, the deep SAMoCo is particularly useful for cases with unsteady breathing, compensation of residual motion during a breath-hold scan, or scans with fast gantry rotation times in which the data acquisition only covers a very limited number of breathing cycles. Furthermore, not requiring gating signals may simplify the clinical workflow and reduces the time needed for patient preparation.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent space reconstruction for missing data problems in CT.","authors":"Anton Kabelac, Elias Eulig, Joscha Maier, Maximilian Hammermann, Michael Knaup, Marc Kachelrieß","doi":"10.1002/mp.17910","DOIUrl":"https://doi.org/10.1002/mp.17910","url":null,"abstract":"<p><strong>Background: </strong>The reconstruction of a computed tomography (CT) image can be compromised by artifacts, which, in many cases, reduce the diagnostic value of the image. These artifacts often result from missing or corrupt regions in the projection data, for example, by truncation, metal, or limited angle acquisitions.</p><p><strong>Purpose: </strong>In this work, we introduce a novel deep learning-based framework, latent space reconstruction (LSR), which enables correction of various types of artifacts arising from missing or corrupted data.</p><p><strong>Methods: </strong>First, we train a generative neural network on uncorrupted CT images. After training, we iteratively search for the point in the latent space of this network that best matches the compromised projection data we measured. Once an optimal point is found, forward-projection of the generated CT image can be used to inpaint the corrupted or incomplete regions of the measured raw data.</p><p><strong>Results: </strong>We used LSR to correct for truncation and metal artifacts. For the truncation artifact correction, images corrected by LSR show effective artifact suppression within the field of measurement (FOM), alongside a substantial high-quality extension of the FOM compared to other methods. For the metal artifact correction, images corrected by LSR demonstrate effective artifact reduction, providing a clearer view of the surrounding tissues and anatomical details.</p><p><strong>Conclusions: </strong>The results indicate that LSR is effective in correcting metal and truncation artifacts. Furthermore, the versatility of LSR allows its application to various other types of artifacts resulting from missing or corrupt data.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiotherapy class-solution to correct an energy-dependent optically stimulated luminescence film dosimeter.","authors":"Marco Caprioli, Arnaud Colijn, Laurence Delombaerde, Robin De Roover, Vanstraelen Bianca, Wouter Crijns","doi":"10.1002/mp.17920","DOIUrl":"https://doi.org/10.1002/mp.17920","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patient-Specific Quality Assurance in Radiotherapy (PSQA) demands high-resolution dosimetry to verify accurate dose delivery in personalized intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) treatments. A novel optically stimulated luminescence (OSL) film dosimeter made with BaFBr:Eu&lt;sup&gt;2+&lt;/sup&gt; phosphor, offers submm spatial resolution. However, its energy-dependent response, requires corrections. Previously, a correction was proposed for a class of prostate cancer treatments assuming similar OSL energy response within the class.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study explored other class-specific corrections using a comprehensive radiotherapy treatment dataset. New classes were formed based on the similarity of treatment parameters without the need for user-based classifications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The dataset comprised 101 IMRT and VMAT treatment plans for three different Varian linac types (2 × Halcyon, 2 × TrueBeam, and 1 × TrueBeam STx). The treatment classes are based on a K-means clustering algorithm, that utilizes twelve quantitative treatment parameters expressed in principal components. Within cluster sum square (WCSS) was used to find the optimal number of classes and prevent data-overfitting. This objective assignment to classes was compared with three independent manual classifications by experienced medical physicists and dosimetrist. Additionally, a random class assignment was conducted for comparison. The adjusted-random-index (ARI) measured the similarity between classification methods. The OSL film, produced by Agfa N.V., was calibrated using a 6 MV TrueBeam linac. It was then used to measure treatments in an MULTICube phantom (IBA). Readout was performed in a CR-15 scanner. The local dose difference distribution between the measurement and treatment was characterized using a rational function. Class-specific corrections were developed by averaging the parameters of the rational function for each class as determined by the clustering, manual, and random classification methods. Dosimetric performances were evaluated within 20% and 50% isodose lines (D20% and D50%) before and after correction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The clustering method identified eight clusters (WCSS = 119 silhouette = 0.6) when representing data in three principal components, that is, 75% of the data variance. No significant similarity was found between clustering results and manual classification methods (ARI &lt; 0.01). Manual classifications are subject to interoperator variability. In fact, we found moderate similarity between classes and variations in the number of classes, ranging from 9 to 16. Uncorrected global dose difference (%) had mean value 0.9% ± 4.1% within D20%, with 47 and 34 treatments resulting in dose difference below 3% within D20% and D50%, respectively. After class-specific correction, the clustering method had mean dose differences (%) -0.2% ± 2.0%","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods of causal effect estimation for high-dimensional treatments: A radiotherapy simulation study.","authors":"Alexander Jenkins, Eliana Vasquez Osorio, Andrew Green, Marcel van Herk, Matthew Sperrin, Alan McWilliam","doi":"10.1002/mp.17919","DOIUrl":"https://doi.org/10.1002/mp.17919","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Radiotherapy, the use of high-energy radiation to treat cancer, presents a challenge in determining treatment outcome relationships due to its complex nature. These challenges include its continuous, spatial, high-dimensional, multi-collinear treatment, and personalized nature, which introduces confounding bias.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Existing voxel based estimators may lead to biased estimates as they do not use a causal inference framework. We propose a novel estimator using sparsity via Adaptive Lasso within Pearl's causal framework, the Causal Adaptive Lasso (CAL).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;First, simplified 2-dimensional treatment plans were simulated on &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;10&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mn&gt;10&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$10times 10$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; and &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;25&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mn&gt;25&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$25times 25$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; grids. Each simulation had an organ at risk placed in a consistent location where dose was minimized and a randomly placed target volume where dose was maximized. Treatment uncertainties were simulated to emulated a fractionated delivery. A directed acyclic graph was devised which captured the causal relationship between our outcome, including confounding. The estimand was set to the associated dose-outcome response for each simulated delivery ( &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;n&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;500&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$n=500$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ). We compared our proposed estimator the CAL against established voxel based regression estimators using planned and delivered simulated doses. Three variations on the causal inference-based estimators were implemented: causal regression without sparsity, CAL, and pixel-wise CAL. Variables were chosen based on Pearl's Back-Door Criterion. Model performance was evaluated using Mean Squared Error (MSE) and assessing bias of the recovered estimand.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;CAL is tested on simulated radiotherapy treatment outcome data with a spatially embedded dose response function. All tested CAL estimators outperformed voxel-based estimators, resulting in significantly lower total MSE, &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mtext&gt;MSE&lt;/mtext&gt; &lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;o&lt;/mi&gt; &lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;annotation&gt;$text{MSE}_{tot}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , and bias, yielding up to a four order of magnitude improvement in &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mtext&gt;MSE&lt;/mtext&gt; &lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;o&lt;/mi&gt; &lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;annotation&gt;$text{MSE}_{tot}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; compared to current voxel-based estimators ( &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;MSE&lt;/mtext&gt; &lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;o&lt;/mi&gt; &lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;1&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;msup&gt;&lt;mn&gt;10&lt;/mn&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/mrow&gt; &lt;annotation&gt;$text{MSE}_{tot} &lt; 1 times 10^{2}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; compared to &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;MSE&lt;/mtext&gt; &lt;mrow","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}