Medical physics最新文献

{"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":"10.1002/mp.17911","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To address this issue and to increase temporal resolution, we propose the deep single angle-based motion compensation (SAMoCo).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>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 unstead","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228009","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":"Latent space reconstruction for missing data problems in CT","authors":"Anton Kabelac,&nbsp;Elias Eulig,&nbsp;Joscha Maier,&nbsp;Maximilian Hammermann,&nbsp;Michael Knaup,&nbsp;Marc Kachelrieß","doi":"10.1002/mp.17910","DOIUrl":"10.1002/mp.17910","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228010","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":"Radiotherapy class-solution to correct an energy-dependent optically stimulated luminescence film dosimeter","authors":"Marco Caprioli,&nbsp;Arnaud Colijn,&nbsp;Laurence Delombaerde,&nbsp;Robin De Roover,&nbsp;Vanstraelen Bianca,&nbsp;Wouter Crijns","doi":"10.1002/mp.17920","DOIUrl":"10.1002/mp.17920","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;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;\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 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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&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 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","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,&nbsp;Eliana Vasquez Osorio,&nbsp;Andrew Green,&nbsp;Marcel van Herk,&nbsp;Matthew Sperrin,&nbsp;Alan McWilliam","doi":"10.1002/mp.17919","DOIUrl":"10.1002/mp.17919","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;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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;First, simplified 2-dimensional treatment plans were simulated on &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$10times 10$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;25&lt;/mn&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mn&gt;25&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$25times 25$&lt;/annotation&gt;\u0000 &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.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;The estimand was set to the associated dose-outcome response for each simulated delivery (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;500&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$n=500$&lt;/annotation&gt;\u0000 &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;\u0000 &lt;","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210594","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":"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,&nbsp;Stephen L. Brown,&nbsp;Mohammad M. Ghassemi,&nbsp;Prabhu C. Acharya,&nbsp;James R. Ewing,&nbsp;Indrin J. Chetty,&nbsp;Farzan Siddiqui,&nbsp;Benjamin Movsas,&nbsp;Kundan Thind","doi":"10.1002/mp.17921","DOIUrl":"10.1002/mp.17921","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;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.&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 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.&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;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.&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 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","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","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,&nbsp;Gianluca Verona-Rinati,&nbsp;Stephan Brons,&nbsp;Rainer Cee,&nbsp;Stefan Scheloske,&nbsp;Christian Schömers,&nbsp;Rafael Kranzer,&nbsp;Thomas Haberer,&nbsp;Marco Marinelli,&nbsp;Andrea Mairani,&nbsp;Thomas Tessonnier","doi":"10.1002/mp.17893","DOIUrl":"10.1002/mp.17893","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;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.&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 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.&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 “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.&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 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 (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;) above 0.99.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144201195","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":"Design and characterization of a novel scintillator array for UHDR PBS proton therapy surface dosimetry","authors":"Roman Vasyltsiv,&nbsp;Joseph Harms,&nbsp;Megan Clark,&nbsp;David J. Gladstone,&nbsp;Brian W. Pogue,&nbsp;Rongxiao Zhang,&nbsp;Petr Bruza","doi":"10.1002/mp.17922","DOIUrl":"10.1002/mp.17922","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;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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aims to develop 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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&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% pas","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","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,&nbsp;Shenyan Zong,&nbsp;Chang-Sheng Mei,&nbsp;Guofeng Shen,&nbsp;Yueran Zhao,&nbsp;He Wang","doi":"10.1002/mp.17909","DOIUrl":"10.1002/mp.17909","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 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.&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 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.&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;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.&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;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.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","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,&nbsp;Parvat Sapkota,&nbsp;Damon Sprouts,&nbsp;Xun Jia,&nbsp;Yujie Chi","doi":"10.1002/mp.17915","DOIUrl":"10.1002/mp.17915","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;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. &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 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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192573","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":"Characterization of a practically designed plastic scintillation plate dosimeter","authors":"Takeshi Ohta,&nbsp;Yuki Nozawa,&nbsp;Shingo Ohira,&nbsp;Kanabu Nawa,&nbsp;Hideomi Yamashita,&nbsp;Keiichi Nakagawa","doi":"10.1002/mp.17904","DOIUrl":"10.1002/mp.17904","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>A practical scintillation plate dosimeter was designed and its dosimetric characteristics were evaluated using x-ray beams from a linear accelerator.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The practical scintillation plate dosimeter showed favorable dose characteristics that can be applied in patient-specific quality assurance for volumetric modulated arc therapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192574","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}