Medical physics最新文献

{"title":"Cone beam computed tomography reconstruction from truncated projections using prior information and transfer learning.","authors":"Yiqun Han, Chengyijue Fang, Yunwen Huang, Qiwei Wu, Cheng Zheng, Hui Liu, Yidong Yang","doi":"10.1002/mp.70488","DOIUrl":"10.1002/mp.70488","url":null,"abstract":"<p><strong>Background: </strong>Cone beam computed tomography (CBCT) is widely used in clinical practice and small animal research for image guidance. The reconstruction quality will be compromised by severe truncation-related artifacts when the scanned object is not fully covered by the field of view (FOV).</p><p><strong>Purpose: </strong>This work aims to develop a Dual-Domain Deep learning-based method for CBCT Reconstruction from Truncated projections (D3CRT) through the guidance of non-truncated prior information.</p><p><strong>Methods: </strong>The D3CRT comprised sequential procedures in both projection and image domains. First, in projection domain, a Sinogram Generation Network (SG-Net) based on the denoising diffusion probabilistic model (DDPM) was employed to predict the missing projection data outside the FOV. The SG-Net was fine-tuned via transfer learning using non-truncated prior data to achieve object-specific adaptation. FDK reconstruction was subsequently performed using the predicted projections. Second, in image domain, an Image Enhancement Network (IE-Net) was applied to refine the FDK reconstructed images. Compressed sensing (CS) reconstruction was then carried out to enforce data fidelity by incorporating the original projections, followed by a secondary IE-Net for final image quality enhancement. In-vivo small animal experiments were conducted on a micro-CBCT system to validate the D3CRT method, with non-truncated prior data obtained from large-FOV low-resolution scans. Dice similarity coefficient (DSC), structural similarity index measure (SSIM), root mean square error (RMSE) were used for quantitative evaluation.</p><p><strong>Results: </strong>The proposed D3CRT effectively improves the image reconstruction quality under truncated projection conditions. For whole-body and lung regions, D3CRT achieved DSCs of 97.1% and 96.0%, outperforming the low-resolution prior images (DSCs of 96.8% and 89.9%) when compared with the reference region segmentations. Quantitative evaluations within the FOV yielded an average RMSE of 2.95 <math> <semantics><mrow><mo>×</mo> <mn>1</mn> <msup><mn>0</mn> <mrow><mo>-</mo> <mn>3</mn></mrow> </msup> <mi>m</mi> <msup><mi>m</mi> <mrow><mo>-</mo> <mn>1</mn></mrow> </msup> </mrow> <annotation>$ times 1{{{mathrm{0}}}^{{mathrm{ - 3}}}}{mathrm{m}}{{{mathrm{m}}}^{{mathrm{ - 1}}}}$</annotation></semantics> </math> and an SSIM of 98.1% for D3CRT, demonstrating better performance than the Low Resolution Image Constrained Reconstruction (LRICR) method which directly takes low-resolution prior images as the initial inputs for CS reconstruction (RMSE 3.83 <math> <semantics><mrow><mo>×</mo> <msup><mn>10</mn> <mrow><mo>-</mo> <mn>3</mn></mrow> </msup> <mi>m</mi> <msup><mi>m</mi> <mrow><mo>-</mo> <mn>1</mn></mrow> </msup> </mrow> <annotation>$ times 10^{- 3}{mathrm{m}}{mathrm{m}}^{-1}$</annotation></semantics> </math> , SSIM 97.4%).</p><p><strong>Conclusion: </strong>By leveraging non-truncated prior information","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70488"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147966295","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":"Cross-attention multimodal fusion for EGFR mutation prediction: A predictive model using CT and whole-slide images.","authors":"Yuanshun Jiang, Bao Feng, Yehang Chen, Junhui Xu, Ronggang Li, Weibin Zhang, Xiangmeng Chen, Wansheng Long, Kunwei Li","doi":"10.1002/mp.70450","DOIUrl":"10.1002/mp.70450","url":null,"abstract":"<p><strong>Background: </strong>The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy. Since EGFR mutations manifest as both macroscopic imaging features on CT and microscopic morphological changes in tissue, integrating these multiscale signals is essential for a comprehensive diagnostic assessment. However, current related research faces two key limitations: on one hand, unimodal deep learning models suffer from limited representational power; on the other hand, existing multimodal methods fail to address the inherent data structural discrepancies between continuous CT and discrete WSI, often losing critical fine-grained details due to forced data compression or shared semantic bottlenecks.</p><p><strong>Objective: </strong>To address the above limitations and improve the reliability of EGFR mutation status prediction, this study aims to propose a novel multimodal fusion framework (MFCA) that can effectively capture cross-modal semantic interactions and align imaging features across different scales.</p><p><strong>Methods: </strong>A novel MFCA based on Cross-Attention (MFCA) is proposed, and its implementation steps are as follows: 1. First, a region-of-interest-guided approach is utilized to coarsely segment whole-slide histopathology images (WSI) into three constituent regions, namely cancerous, stromal, and other regions; 2. Then, a dual-branch encoder is employed to separately extract features from two types of imaging data-global features from Computed Tomography (CT) scans and region-specific features from the segmented WSI; 3. Critically, a bidirectional cross-attention module is introduced into the framework, which is designed to facilitate deep semantic interaction and alignment between the macroscopic context of CT imaging and the microscopic context of histopathology, thereby achieving highly efficient and discriminative feature fusion.</p><p><strong>Results: </strong>On the external validation set, our MFCA framework achieved robust performance, with Area Under the Curve (AUC) values of 0.758(95% CI: 0.683-0.832) for cancerous regions, 0.805(95% CI: 0.716-0.900) for stromal regions, and 0.760(95% CI: 0.686-0.833) for other regions. The model's performance, particularly in the stromal component, was statistically superior to all baseline and competing models.</p><p><strong>Conclusion: </strong>The proposed MFCA framework predicts EGFR mutation status by innovatively integrating macroscopic CT imaging with region-specific microscopic WSI features. It serves as a valuable computational tool to support precision oncology for patients with advanced lung adenocarcinoma.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70450"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793198","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":"Integration of dual-energy CT characteristics and biomarkers: Noninvasive prediction of Ki-67 expression in pancreatic ductal adenocarcinoma.","authors":"Wuyang Zhang, Bin Wang, Dan Shi, Yuanyuan Ruan, Peng Cheng, Shuai Ming, Mengru Li, Wei Cao, Min Wang, Jingyu Li, Wei Wei","doi":"10.1002/mp.70452","DOIUrl":"10.1002/mp.70452","url":null,"abstract":"<p><strong>Background: </strong>The Ki-67 proliferation index is a critical prognostic marker in pancreatic ductal adenocarcinoma (PDAC); however, its assessment relies on invasive tissue sampling. Ki-67 expression reflects active tumor cell proliferation and is associated with aggressive tumor behavior. A preoperative, noninvasive method to predict Ki-67 status would therefore be valuable for clinical decision-making. Dual-energy CT (DECT) can provide quantitative parameters related to tumor vascularity and composition, potentially reflecting proliferative activity. Additionally, clinical biomarkers such as CA125 may offer complementary information regarding tumor biology. Therefore, the development of a reliable noninvasive approach to preoperatively determine Ki-67 status is of considerable clinical importance.</p><p><strong>Purpose: </strong>To develop and validate a noninvasive approach for predicting Ki-67 expression in pancreatic ductal adenocarcinoma by integrating quantitative dual-energy CT parameters and clinical biomarkers.</p><p><strong>Methods: </strong>This retrospective study included 148 PDAC patients randomly divided into training (n = 89) and validation (n = 59) sets (6:4 ratio). All patients underwent preoperative DECT scans, and quantitative parameters including normalized iodine concentration (NIC), effective atomic number (Zeff), spectral attenuation slope (λ), etc. were obtained from three contrast phases. Serum tumor markers (CA19-9, CA125, CA50, CEA) and clinical features were analyzed. Multivariate logistic regression was used to identify predictors of Ki-67 expression. A nomogram and 3-D probability surface were developed to intuitively demonstrate the model's predictive structure and decision-making process. Model performance was validated using ROC analysis, calibration curves, and decision curve analysis. Innovatively, kernel-density ridgeline plots and prediction-error bar plots were employed to comprehensively evaluate risk distribution and prediction accuracy, demonstrating the model's stability.</p><p><strong>Results: </strong>The joint model demonstrated excellent predictive performance, achieving AUCs of 0.803 in the training set and 0.810 in the validation set, outperforming both the clinical-only model (training AUC = 0.682, validation AUC = 0.751) and the DECT-only model (training AUC = 0.712, validation AUC = 0.702). Multivariate analysis identified arterial-phase normalized iodine concentration (A-NIC) (p = 0.046) and CA125 (p = 0.005) as independent predictors of Ki-67 expression. These two parameters formed the basis of the final predictive model, demonstrating consistent diagnostic value across both cohorts.</p><p><strong>Conclusion: </strong>Integration of DECT parameters and clinical biomarkers allows accurate noninvasive prediction of Ki-67 expression in PDAC, offering a potential tool for preoperative assessment of tumor proliferation.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70452"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848201","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 preclinical setup for spatially fractionated radiation therapy with electrons.","authors":"Edward R J F Taylor, Jia-Ling Ruan, Salomé Paillas, Iain D C Tullis, Geoff S Higgins, Kristoffer Petersson","doi":"10.1002/mp.70448","DOIUrl":"10.1002/mp.70448","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Spatially fractionated radiation therapy (SFRT) has therapeutic potential as a priming therapy which boosts tumor control. However, the optimal delivery and spatial fractionation parameters have not been deciphered and the mechanisms at play are not yet fully understood.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This paper highlights our preclinical setups for mini-grid SFRT with 6 MeV electrons delivered at conventional to ultrahigh dose rates, using a flexible collimator system. These setups let us explore relevant spatial fractionation parameters to observe their effect on tumor growth and normal tissue toxicity. Preclinical studies here may reveal the parameters of highest clinical relevance for SFRT and combination therapies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;For preclinical experiments with electron spatial fractionation, 6.5 mm thick brass collimators were made with 7- (or 19-) hole hexagonally packed ∅ 0.65-2 mm apertures, with 1.6-5 mm CTC distances. Irradiated EBT-XD Gafchromic film downstream of collimators were analyzed to obtain peak-to-valley dose ratios (PVDR), full width at half maxima (FWHM), and peak doses at various depths in solid water, and at surface when increasing the separations from collimators in air. Male and female C57BL/6 mice were injected subcutaneously with UPPL1541 bladder cancer cells in the right flank. After 10-13 days, a single dose treatment was delivered to the tumors with either ∅14 mm circular homogeneous field (10 Gy delivered at 3 kGy s&lt;sup&gt;-1&lt;/sup&gt;), or using a 7-hole (∅ of 2 and 5 mm center-to-center distances) spatially fractionated field; peak doses of 30 Gy delivered at 820 Gy s&lt;sup&gt;-1&lt;/sup&gt;, 20 Gy at 860 Gy s&lt;sup&gt;-1&lt;/sup&gt;, and 20 Gy at &lt; 0.1 Gy s&lt;sup&gt;-1&lt;/sup&gt;. Tumor growth and time to triple tumor volume (TTTV) were measured and compared between treatment regimens.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Similar PVDRs were obtained with 7- and 19-hole inserts (35 and 31 at surface, respectively). Peak widths increased with depth, and maximal peak dose rates were &gt; 1.8 kGy s&lt;sup&gt;-1&lt;/sup&gt;. A displacement in air from the collimator exit decreased PVDRs at the phantom surface; from 32 to 16 at ∼10 mm distance, and to 6 at ∼20 mm distance. Peak doses also reduced to ∼57 % at 10 mm distance, and to ∼33% at 20 mm distance. Film measurements at the mouse phantom surface produced peak and valley dose rates of &gt; 850 Gy s&lt;sup&gt;-1&lt;/sup&gt; and ∼60 Gy s&lt;sup&gt;-1&lt;/sup&gt; respectively, with a PVDR &gt; 14. Tumor growth delays for spatially fractionated FLASH 30 Gy (peak dose, with a 2.1 Gy valley dose, and a 10 Gy average dose) and homogeneous FLASH 10 Gy electron irradiation regimens were similar. Both regimens also demonstrated significantly longer TTTV compared to control and spatially fractionated conventional 20 Gy (peak dose, with a 1.4 Gy valley dose, and a 6.7 Gy average dose) regimens (p &lt; 0.05). No significant differences in body weight and skin damage were observed, indicating acceptable treatment to","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70448"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GPU-accelerated radiation dose engine for small animal proton radiotherapy.","authors":"Haonian Gong, Yuxiang Wang, Zihao Liu, Meiqi Liu, Yue Gu, Cheng Zheng, Hsiao-Ming Lu, Yidong Yang","doi":"10.1002/mp.70455","DOIUrl":"10.1002/mp.70455","url":null,"abstract":"<p><strong>Background: </strong>Preclinical small animal experiments play an indispensable role in proton therapy research. However, accurate dose calculation poses a significant challenge because of the low beam energy and the requirement for submillimeter spatial resolution. Although the Monte Carlo method offers the necessary precision, its high computational cost hinders efficient implementation.</p><p><strong>Purpose: </strong>This study aims to develop a GPU-accelerated radiation dose engine for proton radiotherapy (pGARDEN) based on the Monte Carlo method, specifically designed for fast and accurate dose calculation in small animal irradiation.</p><p><strong>Methods: </strong>In pGARDEN, we optimized the particle transport algorithm to better align with the GPU architecture. Moreover, various acceleration techniques were implemented to boost computational efficiency. To enhance precision, physical parameters, such as energy cutoffs for proton and electron, were tuned to better suit small animal conditions. The performance of pGARDEN was validated against Geant4 simulations and measurements across various beams and phantoms. To demonstrate its practical utility, pGARDEN was applied to calculate a multi-beam proton treatment plan for a lung tumor-bearing mouse model.</p><p><strong>Results: </strong>Compared to Geant4, the engine achieved a > 1000-fold speedup and a 3D gamma passing rate of > 97% with a strict 1%/0.15 mm criterion in all phantom testing scenarios. The integrated depth dose curves and dose profiles showed good agreement with measurements. In the in vivo validation, the 2D gamma passing rates with a 2%/0.3 mm criterion were 95.52% ± 0.74% for the abdomen and 94.18% ± 1.08% for the thorax. Furthermore, pGARDEN calculated the treatment plan with < 1% statistical uncertainty in 4.3 s on an NVIDIA GeForce RTX 4070 Ti GPU, achieving a 100% 3D gamma passing rate with a 2%/0.3 mm criterion.</p><p><strong>Conclusion: </strong>pGARDEN can calculate proton dose distribution rapidly and accurately at submillimeter resolution for small animal. It provides a valuable tool for supporting small animal proton radiation experiments, such as the investigation of relative biological effectiveness (RBE) and new therapeutic strategies.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70455"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793456","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":"Gamma knife knowledge-based planning with isocenter selection.","authors":"Binghao Zhang, Mark Ruschin, Timothy C Y Chan","doi":"10.1002/mp.70463","DOIUrl":"10.1002/mp.70463","url":null,"abstract":"<p><strong>Background: </strong>In Gamma Knife (GK) radiosurgery, effective treatment planning aims to achieve a desired dose distribution while minimizing the overall treatment duration. Clinically, plans are generated either manually through forward planning, or using inverse planning methods. To address the limitations presented by forward and inverse planning, a knowledge-based planning (KBP) pipeline was recently developed for GK, using 3D dose predictions in combination with inverse optimization. However, this approach still relied on manually selected isocenter locations, which may limit plan quality.</p><p><strong>Purpose: </strong>To develop a complete KBP pipeline for GK, combining dose prediction with the simultaneous optimization of isocenter locations and beam-on-times.</p><p><strong>Methods: </strong>Data for 20 patients were obtained from a local care center. A previously trained deep learning model generated 3D dose predictions for each case. The primary treatment planning model was a mixed-integer model (GK-KBP-OptIso) that combined isocenter selection with beam-on-time optimization. Plans were generated under several different maximum allowable isocenter limits and compared against (1) plans generated using an existing KBP pipeline with fixed, manually selected isocenters (GK-KBP-FixIso) and (2) historical clinical plans. Plan quality was assessed using coverage, selectivity, Paddick conformity, and gradient indices, along with overall treatment time.</p><p><strong>Results: </strong>Under the same isocenter limits, GK-KBP-OptIso achieved comparable conformity to GK-KBP-FixIso ( <math> <semantics><mrow><mn>0.657</mn> <mo>±</mo> <mn>0.161</mn></mrow> <annotation>$0.657 pm 0.161$</annotation></semantics> </math> vs <math> <semantics><mrow><mn>0.684</mn> <mo>±</mo> <mn>0.152</mn></mrow> <annotation>$0.684 pm 0.152$</annotation></semantics> </math> ) while significantly improving dose falloff. It also matched clinical plan quality, though with slightly increased treatment times. However, when allowed to use variable number of isocenters, GK-KBP-OptIso produced plans with a higher average conformity ( <math> <semantics><mrow><mn>0.717</mn> <mo>±</mo> <mn>0.124</mn></mrow> <annotation>$0.717 pm 0.124$</annotation></semantics> </math> ) than clinical plans ( <math> <semantics><mrow><mn>0.662</mn> <mo>±</mo> <mn>0.113</mn></mrow> <annotation>$0.662 pm 0.113$</annotation></semantics> </math> ).</p><p><strong>Conclusions: </strong>A KBP pipeline that integrates isocenter selection yields plans that are equal or superior to those generated using KBP with manually selected isocenters or manual clinical planning. The presented approach holds the potential to streamline the effort required to generate high-quality GK treatment plans.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70463"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13175956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147944365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proton therapy range uncertainty reduction using vendor-agnostic tissue characterization on a virtual photon-counting CT head scan.","authors":"Stevan Vrbaski, Goran Stanic, Silvia Molinelli, Mridul Bhattarai, Ehsan Abadi, Mario Ciocca, Ehsan Samei","doi":"10.1002/mp.70447","DOIUrl":"10.1002/mp.70447","url":null,"abstract":"<p><strong>Background: </strong>Photon-counting CT (PCCT) is the latest technology enabling imaging with reduced noise and inherent spectral separation, with the potential to directly calculate a more accurate tissue stopping power from spectral data. This potential benefit is difficult to quantify in practice and is currently evaluated mainly in phantoms with simplified geometries that only approximate real patient anatomy.</p><p><strong>Purpose: </strong>In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a CT system. In addition, we validate the accuracy of stopping power ratio (SPR) calculations on a model of a PCCT scanner using a conventional stoichiometric calibration approach and a prototype software TissueXplorer.</p><p><strong>Methods: </strong>A validated CT simulator (DukeSim) was used to generate PCCT projections of a computational head phantom, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors. The ground-truth treatment plan was made directly on the computational phantom using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The resulting dose distributions were subsequently compared against the ground-truth plan to quantify dose differences arising from SPR estimation.</p><p><strong>Results: </strong>The mean percentage difference in estimating the SPR with TissueXplorer in all head tissues inside the scanned volume was 0.28%. SPRs obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head phantom.</p><p><strong>Conclusions: </strong>Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information hold promise for more accurate prediction of the SPR than the conventional stoichiometric approach.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70447"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13244498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-resolved point dosimetry for spread-out Bragg-peak proton FLASH using fibre-coupled scintillators.","authors":"Sky R Steenholdt, Jacob G Johansen, Eleni Kanouta, Per R Poulsen","doi":"10.1002/mp.70464","DOIUrl":"10.1002/mp.70464","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In FLASH radiotherapy, the dose is delivered using ultra-high dose rates (UHDR), which are approximately 100 times higher than those used for conventional (CONV) treatments. This has shown promise in sparing normal tissue while maintaining tumour control. Proton beams, particularly in the spread-out Bragg peak (SOBP), offer a favourable depth-dose profile for sparing healthy tissue. However, quality assurance for FLASH requires time-resolved dosimetry to capture the temporal structure of pencil beam scanning delivery. Fibre-coupled scintillating detectors have been used both in electron and photon UHDR beams, and have been applied in the proton beam entrance plateau. Extending their use to the SOBP requires careful calibration to address quenching and water-inequivalent response near the Bragg peak.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To calibrate and validate a fibre-coupled inorganic scintillator detector system for accurate, time-resolved point dosimetry in the SOBP for UHDR proton beams, which will enable preclinical and in-vivo FLASH studies with robust dosimetric and geometric verification.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Experiments were conducted using a clinical proton PBS beam line. A 2D range modulator generated a 5 cm SOBP from a mono-energetic beam. Four ZnSe:O scintillator probes coupled to optical fibres were read out by silicon photomultipliers at 50 kHz. An ionisation chamber provided reference dose measurements. The calibration included determining a signal dependent saturation factor of the silicon photomultiplier &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;k&lt;/mi&gt; &lt;mtext&gt;sat&lt;/mtext&gt;&lt;/msub&gt; &lt;annotation&gt;$k_{text{sat}}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , measuring the absolute calibration factor &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mi&gt;w&lt;/mi&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;15&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;annotation&gt;$N_{D,w,15}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , and characterising the correction for the depth-dependent under-response &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;k&lt;/mi&gt; &lt;mi&gt;Q&lt;/mi&gt;&lt;/msub&gt; &lt;annotation&gt;$k_Q$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; . A calibration validation was performed in the SOBP across a range of UHDR beam currents, evaluating both dosimetric accuracy and probe positional stability. The calibrated system was then used to characterise SOBP beam spot profiles, in terms of full width at half-maximum and dose rate variation with depth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A saturation multiplier of up to 55% was observed across all four probes. The depth-dependent under-response reached up to 12% at the distal SOBP edge. Both effects were successfully corrected for through fitting simple functions. The validation in the SOBP demonstrated that the calibration achieved positional stability within &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; 0.1 mm and agreement between the measured and absolute doses within 0.5% for all probes. Beam characterisation revealed full-width at half-maximum b","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70464"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147871098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage approach for the identification and classification of osteoporotic vertebral compression fractures.","authors":"Yilin Wang, Siyu Chen, Hongwei Zhuang, Yang Yang, Yuhui Kou, Jian Xiong, Peixun Zhang, Dianying Zhang, Xiwen Bai, Maohan Liang","doi":"10.1002/mp.70479","DOIUrl":"10.1002/mp.70479","url":null,"abstract":"<p><strong>Background: </strong>Osteoporotic vertebral compression fractures (OVCFs) are frequently underdiagnosed worldwide due to their subtle radiographic presentation and the inherently low contrast of x-ray images. This diagnostic challenge is further compounded by the limited availability of high-quality annotated datasets.</p><p><strong>Purpose: </strong>In this study, we proposed OVCFinder, a deep-learning-based two-stage cascade model designed for the accurate detection of OVCFs and the classification of old and new fractures in x-ray images.</p><p><strong>Methods: </strong>The segmentation stage is built upon an enhanced DeepLabv3+ backbone, refined to improve local feature sensitivity by reducing the output stride, replacing atrous convolutions with standard ones to suppress noise, and incorporating multiscale convolutional layers to capture morphological variations. In the classification stage, a model library comprising nine diverse deep learning architectures supports both binary and three-class classification tasks, effectively distinguishing normal, new, and old fractures.</p><p><strong>Results: </strong>Experimental results demonstrate that the OVCFinder's performance exceeds that of single-stage detection frameworks. Furthermore, for binary classification, this cascade model obtained an average weighted accuracy of 62.72%, weighted precision of 72.90%, weighted recall of 62.72%, and weighted F1-score of 66.69%. The best individual model performances were achieved by AlexNet, with an accuracy of 70.49%, and VGG16, with an F1-score of 72.36%. These results notably exceeded the average performance of expert physicians (53.42% weighted accuracy, 53.51% weighted precision, 53.42% weighted recall, and 53.16% weighted F1 score). In the more challenging ternary classification task, this model achieved an average weighted accuracy of 37.05%, weighted precision of 50.16%, weighted recall of 22.91%, and weighted F1 score of 30.62%, with the upper-bound performance (ResNet152 reaching 48.09% accuracy and 39.27% F1 score) substantially surpassing that of human experts (39.74% weighted accuracy and 29.49% weighted F1 score).</p><p><strong>Conclusions: </strong>This work represents the first application of a segmentation-classification cascade strategy for OVCF analysis, enabling end-to-end vertebral segmentation and fracture characterization directly on x-ray images. By decomposing the task, applying targeted optimizations, and adopting a modular design, OVCFinder achieves notable advantages in recognition accuracy, robustness, and interpretability. The OVCFinder's performance in both tasks demonstrates its clinical applicability and diagnostic superiority over conventional approaches, offering a low-cost, high-reliability auxiliary diagnostic tool for use in primary healthcare settings.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70479"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147978245","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":"Transformer-based synthesis of 7 Tesla-like T1 contrast from routine clinical magnetic resonance imaging.","authors":"Zach Eidex, Mojtaba Safari, Tonghe Wang, David S Yu, Vanessa Wildman, Hui Mao, Erik Middlebrooks, Aparna H Kesarwala, Xiaofeng Yang","doi":"10.1002/mp.70496","DOIUrl":"10.1002/mp.70496","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Ultra-high-field 7 Tesla (7T) magnetic resonance imaging (MRI) provides improved resolution and signal-to-noise ratio (SNR) over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly, scarce, and introduce additional challenges such as susceptibility artifacts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;We propose an efficient transformer-based model (7T-Restormer) to synthesize 7T-like MRI (quantitative T1 MP2RAGE maps) from routine 1.5T or 3T T1-weighted (T1W) images.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The proposed method leverages an efficient restoration transformer backbone and spatial attention layers to capture long-range dependencies and generate high-quality 7T-like images. Our model was validated on an institutional dataset, comprised of 35 1.5T and 108 3T T1w MRI paired with corresponding 7T T1 maps of patients with confirmed multiple sclerosis (MS). A total of 141 patient cases (32 128 slices) were randomly divided into 105 (25 1.5T and 80 3T) training cases (19 204 slices), 19 (5 1.5T and 14 3T) validation cases (3476 slices), and 17 (5 1.5T and 12 3T) test cases (3145 slices). The synthetic 7T T1 maps were evaluated by comparing their similarity to the ground truth volumes using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE), and were compared against ResViT and ResShift. In addition, a blinded reader study was performed in which three clinicians scored the diagnostic image quality of each method on a 5-point Likert scale (1 = non-acceptable, 5 = excellent). Statistical significance (α = 0.05) was assessed using two-sided paired t-tests with Holm correction for multiple comparisons and Wilcoxon signed-rank tests with Holm correction for reader scores. Effect sizes (Cohen's &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;d&lt;/mi&gt; &lt;mi&gt;z&lt;/mi&gt;&lt;/msub&gt; &lt;annotation&gt;${{d}_z}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; for paired data) were also computed to quantify practical significance. We interpreted &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mo&gt;∣&lt;/mo&gt; &lt;msub&gt;&lt;mi&gt;d&lt;/mi&gt; &lt;mi&gt;z&lt;/mi&gt;&lt;/msub&gt; &lt;mo&gt;∣&lt;/mo&gt; &lt;mo&gt;≈&lt;/mo&gt; &lt;mn&gt;0.2&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mspace&gt;&lt;/mspace&gt; &lt;mn&gt;0.5&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$mid {{d}_z}mid approx 0.2, 0.5,$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; and &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;0.8&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$0.8$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; as small, medium, and large effects.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For 1.5T inputs, 7T-Restormer achieved an NMSE of 0.018 ± 0.003, PSNR of 25.0 ± 0.6 dB, and SSIM of 0.870 ± 0.019; for 3T inputs, the corresponding values were 0.019 ± 0.006, 24.5 ± 1.2 dB, and 0.874 ± 0.027. Across both field strengths, 7T-Restormer provided the lowest NMSE and highest PSNR and SSIM among the three methods, reducing NMSE by approximately 5% and 25% relative to ResShift and ResViT at 1.5T and by 14% and 24% at 3T, respectively, and increasing PSNR by 0.5-1.5 dB (Holm-adjusted p &lt; 0.05, paired Cohen's &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;d&lt;/mi&gt; &lt;mi&gt;","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"53 5","pages":"e70496"},"PeriodicalIF":3.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990432","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}