Medical physics最新文献

{"title":"Characterization of acute radiation-induced vascular changes in animal model of brain tumors using time frequency analysis of DCE MRI information.","authors":"Hassan Bagher-Ebadian, Stephen L Brown, Mohammad M Ghassemi, Prabhu C Acharya, James R Ewing, Indrin J Chetty, Farzan Siddiqui, Benjamin Movsas, Kundan Thind","doi":"10.1002/mp.17921","DOIUrl":"https://doi.org/10.1002/mp.17921","url":null,"abstract":"<p><strong>Background: </strong>Recent studies have confirmed the effects of whole-brain radiation therapy (RT) on the blood-brain-barrier and vasculature permeability. Optimal therapeutic targeting of cancer depends on ability to distinguish tumor from normal tissue.</p><p><strong>Purpose: </strong>This study recruits nested model selection (NMS) and time-frequency analyses of the time-trace of contrast agent from dynamic-contrast-enhanced MRI information to characterize the acute (i.e., within hours) RT response of tumor and normal brain tissues in an animal model of brain tumors.</p><p><strong>Methods: </strong>Twenty immune-compromised-RNU rats were implanted orthotopically with human U251N glioma cells. Twenty-eight days after the brain implantation, two DCE-MRI studies were performed 24 h apart. 20 Gy stereotactic radiation was delivered 1-6.5 h before the second MRI. NMS-based DCE-MRI analysis was performed to distinguish three different brain regions by model selection using a nested paradigm. Model 1 was characterized by non-leaky vasculature and considered as normal brain tissue. Model 2 was characterized by contrast agent (CA) movement predominantly in one direction, out of the vasculature, and was primarily associated with the tumor boundary. In contrast, Model 3 exhibited contrast agent movement in both directions, into and out of the vasculature, and corresponded to the tumor core. Time-traces of CA concentration from pre- and post-RT DCE-MRI data for the different models were analyzed using wavelet-based coherence and wavelet cross-spectrum phase analyses to characterize and rank the magnitude of RT-induced effects. Four distinct time-direction classes (in-phase/anti-phase with lead/lag time) were introduced to describe the impact of RT on CA concentration profiles, allowing for comparison of RT effects across different model-based zones of rat brains.</p><p><strong>Results: </strong>The time-frequency analyses revealed both average lag and lead times between the pre- and post-RT CA concentration profiles for the three model regions. The average lag times were 2.882 s (95% CI: 2.606-3.157) for Model 1, 1.546 s (95% CI: 1.401-1.691) for Model 2, and 2.515 s (95% CI: 2.319-2.711) for Model 3, all exhibiting anti-phase oscillation. The average lead times were 1.892 s (95% CI: 1.757-2.028) for Model 1, 2.632 s (95% CI: 2.366-2.898) for Model 2, and 2.160 s (95% CI: 2.021-2.299) for Model 3, also with anti-phase oscillation. Results imply that compared to pre-RT, Model 1, 2, and 3 regions that correspond to normal tissue, periphery, and core of the tumor, show lag-time (2.882 [2.606 3.157] s), lead-time (2.632 [2.366 2.898] s), and lag-time (2.515 [2.319 2.711] s), in their post-RT time-trace of CA concentration, respectively. RT-induced lead/lag time changes were found to be more significant for the lower frequency components of the CA concentration profiles of all the three models. The analysis further revealed that Model 2 (tumor per","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":"144210593","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":"Characterizing diamond detectors for various dose and dose rate measurements in scanned carbon and oxygen beams.","authors":"Celine Karle, Gianluca Verona-Rinati, Stephan Brons, Rainer Cee, Stefan Scheloske, Christian Schömers, Rafael Kranzer, Thomas Haberer, Marco Marinelli, Andrea Mairani, Thomas Tessonnier","doi":"10.1002/mp.17893","DOIUrl":"https://doi.org/10.1002/mp.17893","url":null,"abstract":"<p><strong>Background: </strong>The emerging FLASH radiotherapy technique employs \"Ultra-High Dose Rate\" (UHDR) irradiations and offers the potential to spare normal tissue while maintaining iso-effective tumor treatment. Given the physical and biological advantages inherent to high \"Linear Energy Transfer\" (LET) particles, the combination of UHDR and high LET has the capability to enhance the normal tissues sparing, as indicated by initial in vivo trials. However, to ensure a safe implementation of this combined modality, it is essential to establish robust dosimetric protocols utilizing dose-, dose rate-, and LET-independent detectors.</p><p><strong>Purpose: </strong>The objective of this study is to characterize the dose, dose rate, and LET dependency of two diamond detectors with high LET carbon and oxygen ion irradiation under \"Standard Dose Rate\" (SDR) and UHDR conditions.</p><p><strong>Methods: </strong>The \"microDiamond\" (mD) and a \"flashDiamond\" (fD) prototype were benchmarked against measurements with a monitoring ionization chamber, Advanced Markus chamber (AMC), and simulations for carbon and oxygen irradiation, with energies of 274.98 MeV/u and 325.98 MeV/u under SDR and UHDR conditions. First, the entire depth-dose profiles obtained during SDR irradiations and the partial in-depth profiles of the Bragg peak region in UHDR were compared to the corresponding simulation values. Secondly, the linearity of the diamond detector response during dose escalation measurements was investigated for both dose rates.</p><p><strong>Results: </strong>The two detectors exhibited alignment with the simulated depth-dose distributions for oxygen and carbon irradiations across both dose rate conditions. The mD overestimated the dose values for carbon and oxygen measurements. This overestimation increased with \"dose-averaged LET\" (LETd) during SDR irradiation and maintained a stable value of 5% for UHDR. Meanwhile, the fD demonstrated a high degree of agreement with the simulation, with a maximum discrepancy of 5% across all irradiation modalities in the plateau and \"Bragg Peak\" (BP). Deviations were observed in the BP fall-off region, while both diamond detectors exhibited a strong alignment with the AMC measurements. Furthermore, both detectors exhibited dose linearity under SDR and UHDR irradiation for both carbon and oxygen irradiation, with a coefficient of determination (R²) above 0.99.</p><p><strong>Conclusion: </strong>In the context of heavy ion carbon and oxygen irradiation in UHDR and SDR, the two diamond detectors demonstrated dose-rate independence. While the mD exhibited a tendency to overestimate dose values with increasing LETd, the fD was found to be LET-independent. The fD appears to offer accurate and reliable dose assessments for UHDR heavy ion experiments.</p>","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":"144201195","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":"Design and characterization of a novel scintillator array for UHDR PBS proton therapy surface dosimetry.","authors":"Roman Vasyltsiv, Joseph Harms, Megan Clark, David J Gladstone, Brian W Pogue, Rongxiao Zhang, Petr Bruza","doi":"10.1002/mp.17922","DOIUrl":"https://doi.org/10.1002/mp.17922","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Ultrahigh dose rate (UHDR) proton therapy has shown promise in normal tissue sparing by enhancing the therapeutic ratio through a method termed the FLASH effect. As in all radiotherapy, accurate in vivo dosimetry is crucial for quality assurance of safe and efficient treatment delivery. However, this remains a challenge for UHDR as existing dosimetry systems lack the spatial and temporal resolution required to verify dose and dose rate in complex anatomical regions, especially for pencil beam scanning (PBS) proton therapy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study aims to develop and evaluate a novel 3D surface dosimetry method for UHDR PBS proton therapy using high-speed imaging of a scintillator array, coupled with stereovision to provide real-time, high-resolution surface dose monitoring during treatment. The spatial, temporal, and dosimetric components of the proposed system are validated via imaging of a custom QA phantom and are compared against a gafchromic film reading of the same field delivered onto a flat surface.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A freely deformable multielement scintillator array was designed with a single element pitch of 7.5 mm and interelement gap of 0.5 mm. Scintillation linearity with dose was evaluated along with the variation in scintillator response with increasing imaging and irradiation angles. Water-equivalent thickness (WET) testing was conducted to evaluate beam attenuation at two energy levels. Scintillation emission in response to dose delivery was imaged at 1000 Hz using a high frame rate camera (BeamSite Ultra, DoseOptics LLC) and the array position was monitored via a 2-camera stereovision system. Imaging system setup was validated using a custom 3D QA phantom to assess spatial accuracy and guide systematic setup correction. Stereovision properties of each array element were used to guide angular emission correction, and geometric transformation to beams-eye-view (BEV). Kernel-based residual spot fitting was applied to derive cumulative dose maps which were then compared to the flat film dose profile of a 5 × 5 cm UHDR PBS delivery using 3%/2 mm gamma analysis. PBS and maximum dose rate maps were also calculated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;System setup achieved an average localization error of 0.62 mm, surpassing the typical 1+ mm threshold used in clinical practice. Intensity correction based on angular information was applied and yielded a cumulative spot dose uncertainty of ∼1% (5.428 mGy). The processed dose map was compared to film via gamma analysis with 3%/2 mm criteria and showed a 99.9% passing rate, indicating high agreement between the planned and measured dose profiles. The WET of the scintillator array was measured to be 1.1 mm, minimizing its impact on dose distribution.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The novel scintillator array system provides accurate, real-time surface dose monitoring with high spatial and temporal resolution, making it a pro","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192575","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":"Accelerated proton resonance frequency-based magnetic resonance thermometry by optimized deep learning method.","authors":"Sijie Xu, Shenyan Zong, Chang-Sheng Mei, Guofeng Shen, Yueran Zhao, He Wang","doi":"10.1002/mp.17909","DOIUrl":"https://doi.org/10.1002/mp.17909","url":null,"abstract":"<p><strong>Background: </strong>Proton resonance frequency (PRF)-based magnetic resonance (MR) thermometry plays a critical role in thermal ablation therapies through focused ultrasound (FUS). For clinical applications, accurate and rapid temperature feedback is essential to ensure both the safety and effectiveness of these treatments.</p><p><strong>Purpose: </strong>This work aims to improve temporal resolution in dynamic MR temperature map reconstructions using an enhanced deep-learning method, thereby supporting the real-time monitoring required for effective FUS treatments.</p><p><strong>Methods: </strong>Five classical neural network architectures-cascade net, complex-valued U-Net, shift window transformer for MRI, real-valued U-Net, and U-Net with residual blocks-along with training-optimized methods were applied to reconstruct temperature maps from 2-fold and 4-fold undersampled k-space data. The training enhancements included pre-training/training-phase data augmentations, knowledge distillation, and a novel amplitude-phase decoupling loss function. Phantom and ex vivo tissue heating experiments were conducted using a FUS transducer. Ground truth was the complex MR images with accurate temperature changes, and datasets were manually undersampled to simulate such acceleration here. Separate testing datasets were used to evaluate real-time performance and temperature accuracy. Furthermore, our proposed deep learning-based rapid reconstruction approach was validated on a clinical dataset obtained from patients with uterine fibroids, demonstrating its clinical applicability.</p><p><strong>Results: </strong>Acceleration factors of 1.9 and 3.7 were achieved for 2× and 4× k-space under samplings, respectively. The deep learning-based reconstruction using ResUNet incorporating the four optimizations, showed superior performance. For 2-fold acceleration, the RMSE of temperature map patches were 0.89°C and 1.15°C for the phantom and ex vivo testing datasets, respectively. The DICE coefficient for the 43°C isotherm-enclosed regions was 0.81, and the Bland-Altman analysis indicated a bias of -0.25°C with limits of agreement of ±2.16°C. In the 4-fold under-sampling case, these evaluation metrics showed approximately a 10% reduction in accuracy. Additionally, the DICE coefficient measuring the overlap between the reconstructed temperature maps (using the optimized ResUNet) and the ground truth, specifically in regions where the temperature exceeded the 43°C threshold, were 0.77 and 0.74 for the 2× and 4× under-sampling scenarios, respectively.</p><p><strong>Conclusion: </strong>This study demonstrates that deep learning-based reconstruction significantly enhances the accuracy and efficiency of MR thermometry, particularly in the context of FUS-based clinical treatments for uterine fibroids. This approach could also be extended to other applications such as essential tremor and prostate cancer treatments where MRI-guided FUS plays a critical role.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192572","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":"Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy.","authors":"Md Mainul Abrar, Parvat Sapkota, Damon Sprouts, Xun Jia, Yujie Chi","doi":"10.1002/mp.17915","DOIUrl":"https://doi.org/10.1002/mp.17915","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Achieving highly efficient treatment planning in intensity-modulated radiotherapy (IMRT) is challenging due to the complex interactions between radiation beams and the human body. The introduction of artificial intelligence (AI) has automated treatment planning, significantly improving efficiency. However, existing automatic treatment planning agents often rely on supervised or unsupervised AI models that require large datasets of high-quality patient data for training. Additionally, these networks are generally not universally applicable across patient cases from different institutions and can be vulnerable to adversarial attacks. Deep reinforcement learning (DRL), which mimics the trial-and-error process used by human planners, offers a promising new approach to address these challenges.  PURPOSE: This work aims to develop a stochastic policy-based DRL agent for automatic treatment planning that facilitates effective training with limited datasets, universal applicability across diverse patient datasets, and robust performance under adversarial attacks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We employ an actor-critic with experience replay (ACER) architecture to develop the automatic treatment planning agent. This agent operates the treatment planning system (TPS) for inverse treatment planning by automatically tuning treatment planning parameters (TPPs). We use prostate cancer IMRT patient cases as our testbed, which includes one target and two organs at risk (OARs), along with 18 discrete TPP tuning actions. The network takes dose-volume histograms (DVHs) as input and outputs a policy for effective TPP tuning, accompanied by an evaluation function for that policy. Training utilizes DVHs from treatment plans generated by an in-house TPS under randomized TPPs for a single patient case, with validation conducted on two other independent cases. Both online asynchronous learning and offline, sample-efficient experience replay methods are employed to update the network parameters. After training, six groups, comprising more than 300 initial treatment plans drawn from three datasets, were used for testing. These groups have beam and anatomical configurations distinct from those of the training case. The ProKnow scoring system for prostate cancer IMRT, with a maximum score of 9, is used to evaluate plan quality. The robustness of the network is further assessed through adversarial attacks using the fast gradient sign method (FGSM).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Despite being trained on treatment plans from a single patient case, the network converges efficiently when validated on two independent cases. For testing performance, the mean &lt;math&gt;&lt;semantics&gt;&lt;mo&gt;±&lt;/mo&gt; &lt;annotation&gt;$pm$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; standard deviation of the plan scores across all test cases before ACER-based treatment planning is &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;6.17&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;1.90&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$6.17 pm 1.90$&lt;/annotation&gt;&lt;/sema","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192573","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 practically designed plastic scintillation plate dosimeter.","authors":"Takeshi Ohta, Yuki Nozawa, Shingo Ohira, Kanabu Nawa, Hideomi Yamashita, Keiichi Nakagawa","doi":"10.1002/mp.17904","DOIUrl":"https://doi.org/10.1002/mp.17904","url":null,"abstract":"<p><strong>Background: </strong>Advancements in radiotherapy have enabled the use of high-definition irradiation, leading to more precise and finely adjusted treatments in clinical settings. Nevertheless, the attainment of high resolution, an extensive measurement area, and the repeatability of dose distribution measurements persist as challenges in clinical practice, thereby often requiring multiple dosimetry systems to overcome measurement constraints. Consequently, there is a significant need to develop a dosimeter that offers both a high resolution and a capability for repeated use.</p><p><strong>Purpose: </strong>A practical scintillation plate dosimeter was designed and its dosimetric characteristics were evaluated using x-ray beams from a linear accelerator.</p><p><strong>Methods: </strong>A practical scintillation plate dosimeter comprised a 0.2 cm-thick scintillation plate sandwiched between a pair of 2.0 cm-thick Polymethyl methacrylate (PMMA) plates. A Complementary Metal Oxide Semiconductor (CMOS) camera was used to detect the scintillation light emitted from the scintillation plate when the x-ray beams were delivered to the plate. Measurements were made at 6 MV to test the dose linearity, reproducibility, and dependencies on the camera temperature and angles of incidence. The dose-rate dependency was also measured using 6 and 10 MV flattening filter-free (FFF) beams. The x-ray energy dependency was further tested using 4 MV, 6 MV, 10 MV, 6 MV FFF, and 10 MV FFF beams.</p><p><strong>Results: </strong>A maximum linearity error of 0.4% was observed for doses ranging from 10 to 1000 MU. The coefficient of variation for the dose reproducibility was ± 0.062%, the temperature dependency was 0.07%/°C, and the angular variations were within ± 1.3% after the removal of Cherenkov light. The dose output decreased by 5.0% at 45 MU/min, compared with that at 1300 MU/min with the 6 MV FFF beams, and by 2.0% at 160 MU/min, compared to 1900 MU/min with the 10 MV FFF beams. The dependency of x-ray energy ranged from -2.1% to +1.4%.</p><p><strong>Conclusions: </strong>The practical scintillation plate dosimeter showed favorable dose characteristics that can be applied in patient-specific quality assurance for volumetric modulated arc therapy.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192574","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":"Physical phantom validation of clustering-initiated factorization in dynamic PET.","authors":"Valerie Kobzarenko, Suzanne L Baker, Mustafa Janabi, Woon-Seng Choong, Grant T Gullberg, Youngho Seo, Rostyslav Boutchko, Debasis Mitra","doi":"10.1002/mp.17902","DOIUrl":"https://doi.org/10.1002/mp.17902","url":null,"abstract":"<p><strong>Background: </strong>Dynamic positron emission tomography (PET) enables the quantification of physiological parameters of radiotracers employed in the investigation of neuropsychiatric disorders. We previously introduced a factor analysis-based algorithm, Cluster-Initialized Factor Analysis (CIFA), designed to overcome the problem of specifying reference regions. CIFA is capable of automatically extracting distinct radiotracer binding distributions across many modalities based on the differences in tracer dynamics, and thus can distinguish regions of specific- and non-specific binding without requiring prior segmentation.</p><p><strong>Purpose: </strong>Our goal is to quantitatively validate the ability of CIFA to resolve different dynamic biological processes by comparing the output of the algorithm to an independent benchmark. As an intermediate goal, we aim to create a physical phantom capable of modeling unique aspects of dynamic imaging and to use this phantom as the benchmark in evaluating CIFA.</p><p><strong>Methods: </strong>CIFA was used to reconstruct ¹⁸F-flortaucipir dynamic brain PET datasets acquired at Lawrence Berkeley National Lab. The resulting factor curves served as the foundation for creating dynamic input time-activity curve (TAC) combinations in a physical brain phantom specifically constructed for this purpose. The phantom represented three components: two overlapping tissue types and free radiotracer, constructed with a combination of small hydraulic elements. The physical components were scanned separately to generate a library of images, allowing us to reproduce scans of any duration with prescribed dynamics and realistic partial volume effects. The phantom was designed to produce noisy instances with compartment mixing of dynamic scans with desired activity TACs for free, non-specifically bound, and specifically bound radiotracers. Ten distinct dynamic simulations with varying levels of TAC similarity were estimated with CIFA.</p><p><strong>Results: </strong>We directly evaluated CIFA's performance in analyzing each of the 10 dynamic datasets by computing the Pearson correlation coefficient between the estimated outputs and the ground truth tissue TACs and corresponding tissue distributions. For seven out of 10 modeled dynamics, which captured the full spectrum of realistically expected tissue TAC shapes, the curve correlation of the specific binding tissue was above 95%.</p><p><strong>Conclusions: </strong>This work formulated an innovative process by combining a physical phantom design with PET images for evaluating the application of CIFA in the extraction of dynamic TACs from dynamic PET image data. In most cases the CIFA algorithm accurately reproduced the dynamics of the phantom simulated data.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192576","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":"A GPU-accelerated Monte Carlo dose engine for external beam radiotherapy.","authors":"Zihao Liu, Yuxiang Wang, Yiqun Han, Panpan Hu, Cheng Zheng, Bing Yan, Yidong Yang","doi":"10.1002/mp.17899","DOIUrl":"https://doi.org/10.1002/mp.17899","url":null,"abstract":"<p><strong>Background: </strong>Accurate dose computation is crucial in intensity-modulated radiation therapy. Owing to its high accuracy, Monte Carlo method is considered the gold standard for radiation dose computation. Its efficiency, however, demands continuous improvement.</p><p><strong>Purpose: </strong>This study aims to develop a GPU-accelerated Monte Carlo radiation dose engine (GARDEN) for fast and accurate dose computation in external beam radiotherapy.</p><p><strong>Methods: </strong>In GARDEN simulation, photon and electron transport were modeled using Woodcock tracking and Class II condensed history technique, respectively. To enhance GPU computational efficiency, warp convergence optimization and coalesced access methods were employed. A novel linear accelerator (Linac) head model was established by incorporating a virtual source and a digital collimator system. The physics was verified against GEANT4 in both homogeneous and heterogeneous phantoms. The Linac head model was commissioned using data measured in a water tank and validated by comparing simulation with film doses for two alternating open and closed MLC patterns. Finally, computational efficiency and accuracy were further evaluated in clinical IMRT and VMAT treatment plans.</p><p><strong>Results: </strong>GARDEN was more than 2500 times faster than GEANT4, with dose differences less than 1% in both homogeneous water and heterogeneous water-lung-bone phantoms. Compared to commission data, the average differences in percentage depth dose curves were less than 1%, and the penumbra differences in lateral dose profiles were less than 1 mm for various radiation field sizes. For two MLC patterns, the gamma pass rates between GARDEN simulations and films were 98.78% and 97.30% at 2%/2 mm criteria, respectively. Both IMRT and VMAT treatment plans achieved gamma pass rates exceeding 99.23% at 3%/3 mm criteria compared to GEANT4 results, with GARDEN completing the dose calculations within 3 s at ∼1% uncertainty on an i9-13900K CPU and NVIDIA 4080 GPU.</p><p><strong>Conclusion: </strong>The accuracy and efficiency of GARDEN has been benchmarked against GEANT4 and validated in both phantoms and clinical treatment plans. With its capability for fast and accurate dose computation, GARDEN shows strong potential for applications in treatment planning and quality assurance.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176257","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":"Multimodal medical image-to-image translation via variational autoencoder latent space mapping.","authors":"Zhiwen Liang, Mengjie Cheng, Jinhui Ma, Ying Hu, Song Li, Xin Tian","doi":"10.1002/mp.17912","DOIUrl":"https://doi.org/10.1002/mp.17912","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Medical image translation has become an essential tool in modern radiotherapy, providing complementary information for target delineation and dose calculation. However, current approaches are constrained by their modality-specific nature, requiring separate model training for each pair of imaging modalities. This limitation hinders the efficient deployment of comprehensive multimodal solutions in clinical practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop a unified image translation method using variational autoencoder (VAE) latent space mapping, which enables flexible conversion between different medical imaging modalities to meet clinical demands.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We propose a three-stage approach to construct a unified image translation model. Initially, a VAE is trained to learn a shared latent space for various medical images. A stacked bidirectional transformer is subsequently utilized to learn the mapping between different modalities within the latent space under the guidance of the image modality. Finally, the VAE decoder is fine-tuned to improve image quality. Our internal dataset collected paired imaging data from 87 head and neck cases, with each case containing cone beam computed tomography (CBCT), computed tomography (CT), MR T1c, and MR T2W images. The effectiveness of this strategy is quantitatively evaluated on our internal dataset and a public dataset by the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Additionally, the dosimetry characteristics of the synthetic CT images are evaluated, and subjective quality assessments of the synthetic MR images are conducted to determine their clinical value.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The VAE with the Kullback‒Leibler (KL)-16 image tokenizer demonstrates superior image reconstruction ability, achieving a Fréchet inception distance (FID) of 4.84, a PSNR of 32.80 dB, and an SSIM of 92.33%. In synthetic CT tasks, the model shows greater accuracy in intramodality translations than in cross-modality translations, as evidenced by an MAE of 21.60 ± 8.80 Hounsfield unit (HU) in the CBCT-to-CT task and 45.23 ± 13.21 HU/47.55 ± 13.88 in the MR T1c/T2w-to-CT tasks. For the cross-contrast MR translation tasks, the results are very close, with mean PSNR and SSIM values of 26.33 ± 1.36 dB and 85.21% ± 2.21%, respectively, for the T1c-to-T2w translation and 26.03 ± 1.67 dB and 85.73% ± 2.66%, respectively, for the T2w-to-T1c translation. Dosimetric results indicate that all the gamma pass rates for synthetic CTs are higher than 99% for photon intensity-modulated radiation therapy (IMRT) planning. However, the subjective quality assessment scores for synthetic MR images are lower than those for real MR images.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The proposed three-stage approach successfully develops a unified image translation model that can effectively handle a wide range of medical image translat","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176273","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":"Experimental validation of Geant4 nuclear interaction models in dose calculations of therapeutic carbon ion beams.","authors":"Yihan Jia, Martina Favaretto, Lisa Hartl, Markus Stock, Dietmar Georg, Loïc Grevillot, Andreas F Resch","doi":"10.1002/mp.17906","DOIUrl":"https://doi.org/10.1002/mp.17906","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The choice of nuclear interaction models in Monte Carlo simulations affects the dose calculation accuracy for light ion beam therapy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study aimed to evaluate the dose calculation accuracy and simulation time of three GATE-RTiON/Geant4 physics lists for therapeutic carbon ion beams, assessing their suitability for independent dose calculation in patient-specific quality assurance (PSQA).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The normalized beam models for physics lists QGSP_BIC_HP_EMZ, QGSP_INCLXX_HP_EMZ, and Shielding_EMZ were validated against measurements regarding the accuracy of range, spot size and reference dose. Normalized transversal dose profiles ( &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$D/D_{max}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ) and field size factor (FSF) were compared with measurements. The accuracy of simulated target dose in 103 fields (various energies, field sizes, depths, and dose gradient &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mo&gt;∇&lt;/mo&gt; &lt;mi&gt;D&lt;/mi&gt;&lt;/msub&gt; &lt;annotation&gt;$nabla _D$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; complexity) of energy-modulated scanned beams was evaluated at 3181 positions. The median of global dose difference &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;d&lt;/mi&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;Δ&lt;/mi&gt; &lt;mi&gt;D&lt;/mi&gt;&lt;/msub&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$med(Delta _D)$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; was calculated at different depth ranges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The three physics lists with validated beam models showed similar accuracy in &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$D/D_{max}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; and FSF in the Bragg peak region and proximal depths, while QGSP_INCLXX_HP agreed most closely for &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt; &lt;mi&gt;a&lt;/mi&gt; &lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$D/D_{max}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; in the fragmentation tail. Accounting for &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mo&gt;∇&lt;/mo&gt; &lt;mi&gt;D&lt;/mi&gt;&lt;/msub&gt; &lt;annotation&gt;$nabla _D$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -related uncertainty, &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;d&lt;/mi&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;Δ&lt;/mi&gt; &lt;mi&gt;D&lt;/mi&gt;&lt;/msub&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$med(Delta _D)$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; remained within ±1.1% for QGSP_INCLXX_HP, while exhibiting an overall increasing trend with depth for QGSP_BIC_HP (up to 2.3%) and a decreasing trend for Shielding (down to -4.1%), respectively. By tuning the number-of-primaries/monitor unit conversion ( &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;k&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;N&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;mi&gt;MU&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;annotation&gt;$k_{rm N/MU}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ) as a function of energy, &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;d&lt;/mi&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;Δ&lt;/mi&gt; &lt;mi&gt;D&lt;/mi&gt;&lt;/msub&gt; &lt;mo&gt;)","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183239","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}