Medical physics最新文献

{"title":"Dual-[18FMISO + 18FLT] PET/CT and MRI imaging in glioblastoma","authors":"Sadek A. Nehmeh, Chang Cui, Rajiv Magge, Theodore H. Schwartz, Jazmin Schwartz, Benjamin Liechty, Phelipi Schuck, Stefaan Guhlke, William Calimag, Ramon F Barajas, Dan Kadrmas, Howard Fine, Jana Ivanidze","doi":"10.1002/mp.18124","DOIUrl":"10.1002/mp.18124","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Tumor hypoxia and proliferation are independent predictors of poor prognosis in glioblastoma and WHO grade 4 IDH-mutant astrocytoma, and are closely linked and can synergistically contribute to local recurrence (LR) and poor overall survival (OS). These two hallmarks can be imaged using FMISO and FLT PET, but only on different days due to the PET intrinsic limitation, which jeopardizes the clinical feasibility and accuracy of multi-parametric studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>In this study, we assess the feasibility of dual-[FMISO+FLT]-PET in a cohort of patients with glioblastoma and WHO grade 4 IDH-mutant astrocytoma.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Eight patients underwent 90 min dynamic PET (dynPET) with staggered FMISO/FLT injections followed by two 10 min scans at 120 and 180 min post-FMISO injection, respectively. The target volume (TV) was delineated on the 180-min imageset. The FMISO input function (IF) was derived from dynPET images of the carotids using the first 50 min, and then extrapolated to the rest of dynPET using a 3-exp fit. The IF<sub>FLT</sub> was deduced by subtracting the IF<sub>FMISO</sub> from IF<sub>FMISO+FLT</sub> over the range > 50 min. The FMISO and FLT kinetic rate constants (KRCs) of the TV and cerebellar cortex (reference tissue) were estimated using kinetic modeling (KM) with a parallel dual-1-tissue-2-compartment model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Seven out of eight patients with a total of 13 lesions completed the study. All lesions were [FMISO+FLT]-avid at 180 min post-FMISO injection with a mean SUVR of 1.72 (range:1.26–3.23). IDH-mutant WHO grade 4 astrocytomas showed reduced tumor hypoxia. Mean lesion KRCs were K<sub>1-FMISO </sub>= 0.18 mL/cc/min (range:0.042–0.432), k<sub>i-FMISO </sub>= 0.011 min<sup>−1</sup> (range:0.00–0.039), K<sub>1-FLT </sub>= 0.103 mL/cc/min (range: 0.004–0.357), and K<sub>i-FLT </sub>= 0.014 mL/min/g (range: 0.00–0.062). Cerebellar cortex KRCs were K<sub>1-FMISO </sub>= 0.098 mL/cc/min (range:0.055–0.225), k<sub>i-FMISO </sub>= 0.008 min<sup>−1</sup> (range:0.002–0.014), K<sub>1-FLT </sub>= 0.089 mL/cc/min (range: 0.001–0.299), and K<sub>i-FLT </sub>= 0.003 mL/min/g (range:0.00–0.007). Lesion perfusion and hypoxia were inversely correlated (<i>R</i> = 0.99).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Dual-[FMISO+FLT]-PET can provide detailed characterization of tumor microenvironment and interaction of multiple hallmarks that yield radio-resistance. This can improve the accuracy of im","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246125","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":"Correction to “Impact of bowtie filter and detector collimation on multislice CT scatter profiles: A simulation study”","authors":"","doi":"10.1002/mp.17978","DOIUrl":"10.1002/mp.17978","url":null,"abstract":"<p>Liu R, Zhang S, Zhao T, et al. Impact of bowtie filter and detector collimation on multislice CT scatter profiles: A simulation study. <i>Med Phys</i>. 2021;48:852-870. https://doi.org/10.1002/mp.14652</p><p>Using Monte Carlo techniques, our group previously reported on the impact of the bowtie filter (BTF), antiscattter grid (ASG), and detector collimation on x-ray projections for a multi-slice fan-beam computed tomography (CT) scanner.<span><sup>1</sup></span> Subsequent simulations performed by Daniel Arroyo-Portilla using a more detailed detector-array model failed to reproduce some of the results of the earlier study and led to discovery of an error in the original simulation code. Daniel Arroyo-Portilla investigated the data used in the original paper, found and corrected errors in the code, and with the efforts of authors J. F. Williamson, J. A. O'Sullivan, M. Porras-Chaverri, and B. Whiting prepared this correction. The goal of this correction is to describe this error and present corrected tables and figures. Because the software error substantially overestimated the x-ray scatter only at large fan angles, the trends and conclusions of the original study are unchanged.</p><p>To improve the efficiency the Monte Carlo simulations, our code employed an analytical model of the antiscatter grid, similar in spirit to the classic work of Day and Dance,<span><sup>2</sup></span> except generalized to cylindrically focused ASGs characteristic of third-generation CT gantries. Briefly, GEANT-4 was used to simulate full Monte Carlo transport of photons through the bowtie filter and the patient to a reference plane located 20.5 cm below the isocenter. For each photon emerging from the reference plane that intersects the detector module, ray tracing through the involved ASG septa and detector elements was performed and an exponential transmission correction applied to the resultant energy-deposition events. The error in the original code involved incorrect assignment of the angle between each septum and the photon trajectory. This resulted in progressively larger attenuation underestimates with increasing fan angle. As Figure 1 shows, the error introduced an anomalous bimodal shape into the scatter-to-primary ratio (SPR) profiles and overestimated the side lobes of the normalized scatter profile (NSP) results.</p><p>The analytical ASG transmission Day/Dance-like algorithm was redesigned and implemented in the same GEANT-4 code environment as used in the original study. The corrected algorithm was carefully validated against independent GEANT-4 ray-tracing calculations and full Monte Carlo simulation of the entire detector and ASG module. This correction presents corrected figures and tables for all results that exhibited statistically significant differences.</p><p>Most of the figures below are presented without commentary. In a few instances, the authors felt that the text in the original paper<sup>1</sup> was unclear or incomplete. In such cases, ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240661","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":"AAPM task group 317 report: A joint AAPM and ESTRO report on brachytherapy catheter, needle, and applicator tracking technology","authors":"Luc Beaulieu,&nbsp;Christoph Bert,&nbsp;Maxence Borot de Battisti,&nbsp;Robert A. Cormack,&nbsp;J. Adam M. Cunha,&nbsp;Antonio L. Damato,&nbsp;Christopher L. Deufel,&nbsp;Gilion L. T. F. Hautvast,&nbsp;I-Chow Hsu,&nbsp;Inger-Karine K. Kolkman-Deurloo,&nbsp;Marinus A. Moerland,&nbsp;Yury Niatsetski,&nbsp;Robert A. Weersink,&nbsp;Kari Tanderup","doi":"10.1002/mp.70037","DOIUrl":"https://doi.org/10.1002/mp.70037","url":null,"abstract":"<p>In recent years, various tracking technologies that work independently of imaging systems have been proposed to automate, simplify, and enhance various tasks in the brachytherapy treatment workflow. These tasks, critical to the overall accuracy of the therapeutic dose delivery, include applicator, catheter and needle insertion guidance, and reconstruction as well as transfer tube connection in afterloading technique. Task Group 317 was established as a joint American Association of Physicists in Medicine (AAPM) and European Society for Radiotherapy and Oncology (ESTRO) committee to review: the current state-of-the-art scientific literature as it pertains to tracking technology in the field of brachytherapy; the benefits and issues related to the use of the technology for automated reconstruction of brachytherapy implants, quality control (QC) tasks such as channel path and tip reconstruction, and real-time guidance tasks; their limitations, in particular in the clinical environment and, finally, to develop recommendations related to commissioning, quality assurance (QA) and clinical use. The Task Group has looked in detail at key tracking technologies in advanced brachytherapy applications: infrared, electromagnetic, fiber optic shape sensing (fiber Bragg grating), and active radiofrequency coil tracking. For each, the performance and accuracy in well-controlled conditions as well as in clinically relevant environments are provided. Guidelines for clinical implementations, including target accuracy and performance needed for critical tasks, are summarized. Risk-based analysis is discussed in the context of an electromagnetic-based tracking system used as part of a clinical trial. The report concludes with the essential elements of an effective quality management program dedicated to the advanced features enabled by the above-described technology.</p>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271716","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":"User evaluation of detector performance in clinical photon-counting and energy-integrating CT scanners using DICOM images","authors":"Ke Li,&nbsp;Xinming Liu,&nbsp;Megan C. Jacobsen,&nbsp;John Rong,&nbsp;Corey T. Jensen,&nbsp;Eric P. Tamm,&nbsp;Frank Dong","doi":"10.1002/mp.70045","DOIUrl":"10.1002/mp.70045","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;Clinical users have a critical need to routinely assess the performance of photon-counting detectors (PCDs) in PCD-CT scanners. Such assessments provide insights into detector characteristics, support protocol optimization, and inform decisions on future scanner acquisitions. Historically, this has been challenging due to limited access to raw detector data, which restricts direct evaluation of PCD performance.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate the zero-frequency detective quantum efficiency (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;DQE&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm DQE}_0$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and detector deadtime of PCDs from an end-user perspective using reconstructed DICOM images.&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;Detector performance was evaluated on two Siemens NAEOTOM Alpha PCD-CT scanners and one Siemens SOMATOM Force energy-integrating detector CT (EID-CT) scanner. Air-only scans were performed in service mode across a range of tube potentials (70–140 kV) and tube currents (4–1200 mA). DICOM images were reconstructed on the scanner using a linear algorithm with a soft-tissue kernel (Br44). The noise power spectrum (NPS) of the images was used to estimate the mean detector output counts. Mean input photon numbers were estimated based on beam quality and exposure measurements. For the PCD-CT systems, tube current-sweep experiments were used to generate image variance—mA curves, from which detector deadtime was estimated using a previously validated parametric model.&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 EID and PCD demonstrated comparable &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;DQE&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm DQE}_0$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; values (EID: 72%–74%; PCD: 72%–77%). &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;DQE&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm DQE}_0$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; showed no significant dependence on tube potential. The estimated PCD deadtime ranged from 5.3 to 7.0 ns. De","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234787","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":"Prior-guided automatic delineation of post-radiotherapy gross tumor volume for esophageal cancer","authors":"Hongfei Sun,&nbsp;Ziqi An,&nbsp;Wei Huang,&nbsp;Qifeng Wang,&nbsp;Yufen Liu,&nbsp;Zihan Shi,&nbsp;Jie Li,&nbsp;Fan Meng,&nbsp;Jie Gong,&nbsp;Lina Zhao","doi":"10.1002/mp.70005","DOIUrl":"10.1002/mp.70005","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Integrating post-radiotherapy (RT) CT into longitudinal esophageal cancer response models substantially improves predictive accuracy. However, manual delineation of gross tumor volume (GTV) on post-RT CT is both labor-intensive and time-consuming.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We propose a novel deep learning–based framework that integrates medical physics priors—pre-RT GTV contours and radiotherapy dose distributions—to automatically delineate post-RT GTV.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multicenter retrospective cohort of 294 EC patients (225 training, 45 internal validation, 24 external validation) was assembled. Pre-RT CT scans, GTV contours, and dose map were co-registered and cropped to 256 × 256. We implemented an nnU-Net v2 backbone, incorporating high dose region and pre-RT GTV priors via element-wise multiplication and element-wise addition to guide feature extraction. Performance was evaluated using anatomical (Dice, IoU, HD95, ASSD, Precision, Recall) and radiomics analyses (ICC, Pearson correlation, LASSO-Cox, C-index) across internal and external cohorts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In cross-validation, the optimal fold achieved DSC = 0.7809 ± 0.1310, IoU = 0.6486 ± 0.1507, HD95 = 3.6321 ± 2.0942, and ASSD = 1.9673 ± 1.0352 (<i>p</i> &lt; 0.0167 vs. state-of-the-art models). Ablation studies demonstrated that combining two types of medical physics priors outperformed single-prior or no-prior models (internal: DSC = 0.7723 ± 0.1290; external: DSC = 0.7545 ± 0.1058). Radiomic features extracted from automated contours exhibited high reproducibility (78.6% with ICC &gt; 0.75) and strong concordance with manual features (<i>R</i> &gt; 0.8), yielding comparable prognostic performance (C-index Δ nonsignificant).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>By embedding medical physics priors into a self-configuring nnU-Net v2, our method achieves accurate and robust automated delineation of post- RT GTV in EC across multiple centers. This approach has potential to facilitate the construction of treatment response prediction models.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234747","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":"Three-dimensional proton FLASH dose rate measurement at high spatiotemporal resolution using a novel multi-layer strip ionization chamber (MLSIC) device","authors":"Shuang Zhou,&nbsp;Arash Darafsheh,&nbsp;Zhiyan Xiao,&nbsp;Anthony Mascia,&nbsp;Yongbing Zhang,&nbsp;Jun Zhou,&nbsp;Liyong Lin,&nbsp;David Zhang,&nbsp;Liuxing Shen,&nbsp;Hao Jiang,&nbsp;Qinghao Chen,&nbsp;Tianyu Zhao,&nbsp;Stephanie Perkins,&nbsp;Tiezhi Zhang","doi":"10.1002/mp.70033","DOIUrl":"10.1002/mp.70033","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Currently, proton therapy is the main radiation treatment modality that can treat deeply seated targets at ultra-high dose rates. The safe translation of FLASH RT into clinic requires dedicated dosimeters capable of measurements at sufficiently high spatiotemporal resolution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The objective of this work is to demonstrate the feasibility of three-dimensional (3D) measurements of dose rate and dose for FLASH pencil beam scanning (PBS) proton therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multi-layer strip ionization chamber (MLSIC) device, along with a reconstruction algorithm, was designed and developed to reconstruct dose and dose rate distribution over a 3D volume. Our MLSIC is composed of 66 layers of strip ionization chamber arrays with total water-equivalent thickness (WET) of 19.2 cm along the beam direction. The first two layers, composed of 128 channels with orthogonal direction with respect to each other, provide the (<i>x,y</i>) coordinate. The other 64 layers contain 32 channels with 8 mm lateral spacing. Data readout at a high-speed of 6250 fps allows spot-by-spot measurement. To prove the concept, PBS proton therapy plans were delivered at conventional and FLASH dose rates. Dose and dose rate information were reconstructed in 3D using an in-house reconstruction algorithm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Ion recombination remained under 1% in the majority of cases. 3D dose reconstruction showed agreement with the treatment planning software; the 3D gamma analysis of the reconstructed dose showed 96.2% (5 mm/5%) and 86.8% (3 mm/3%) passing rates with 10% threshold for the conventional dose rate plan, 99.1% (5 mm/5%) and 92.6% (3 mm/3%) passing rates for the FLASH dose rate plan. 3D dose rate distributions were successfully generated using different definitions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our MLSIC device allows obtaining 3D dose and dose rate distribution of PBS proton beams at FLASH dose rates with high spatiotemporal resolution.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234778","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":"An edge enhanced 3D mamba U-Net for pediatric brain tumor segmentation with transfer learning","authors":"Xiaoyan Sun,&nbsp;Wenhan He,&nbsp;Jianing Ruan,&nbsp;Zhenming Yuan,&nbsp;Zhexian Sun,&nbsp;Jian Zhang","doi":"10.1002/mp.70002","DOIUrl":"10.1002/mp.70002","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Pediatric gliomas, particularly high-grade subtypes, are highly aggressive tumors with low survival rates, and their segmentation remains challenging due to distinct imaging characteristics and data scarcity. While deep learning models perform well in adult glioma segmentation, they struggle with pediatric gliomas, particularly in segmenting complex regions such as the tumor core (TC) and enhancing tumor (ET).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study proposes a solution to address the dual challenges of complex tumor morphology and limited pediatric data in MRI-based pediatric brain tumor segmentation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A solution utilizing an edge-enhanced 3D Mamba U-Net model combined with transfer learning was proposed for pediatric brain tumor segmentation. The network integrated U-Net's multi-scale feature extraction with Mamba's global dependency modeling, augmented by a Mamba residual (MR) block. An edge enhancement (EE) module was embedded in the skip-connection layers to refine boundary detection and capture local features in small pediatric tumor regions. Finally, a non-encoder fine-tuning (NEF) strategy was adopted to adapt the pre-trained adult model to pediatric data by updating only the final reconstruction stage while preserving learned representations. The model was pre-trained on the BraTS 2021 dataset (1251 adult glioma training cases) and fine-tuned on the BraTS-PEDs 2023 dataset (99 pediatric glioma training cases, split 7:1:2 for training, validation, and testing).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>On the BraTS-PEDs 2023 dataset, the method achieved average Dice scores of 0.8917 (WT), 0.8557 (TC), and 0.6365 (ET), with corresponding Hausdorff distances of 3.82, 5.14, and 3.53. The proposed method outperformed the baseline and existing pediatric glioma segmentation approaches included in our experiments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The 3D Mamba U-Net with transfer learning and edge-enhancement modules effectively alleviates the challenges of complex tumor boundaries and small sample size problem in pediatric glioma segmentation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234786","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":"Cone beam CT will replace CT-simulation in near future for radiotherapy planning","authors":"David J. Sher,&nbsp;Mihaela Rosu-Bubulac,&nbsp;Indra J. Das","doi":"10.1002/mp.70044","DOIUrl":"10.1002/mp.70044","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234729","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":"Teaching AI for Radiology Applications: A Multisociety‑Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM","authors":"Felipe Kitamura,&nbsp;Timothy Kline,&nbsp;Daniel Warren,&nbsp;Linda Moy,&nbsp;Roxana Daneshjou,&nbsp;Farhad Maleki,&nbsp;Igor Santos,&nbsp;Judy Gichoya,&nbsp;Walter Wiggins,&nbsp;Brian Bialecki,&nbsp;Kevin O'Donnell,&nbsp;Adam E. Flanders,&nbsp;Matt Morgan,&nbsp;Nabile Safdar,&nbsp;Katherine P. Andriole,&nbsp;Raym Geis,&nbsp;Bibb Allen,&nbsp;Keith Dreyer,&nbsp;Matt Lungren,&nbsp;Monica J. Wood,&nbsp;Marc Kohli,&nbsp;Steve Langer,&nbsp;George Shih,&nbsp;Eduardo Farina,&nbsp;Charles E. Kahn Jr.,&nbsp;Ingrid Reiser,&nbsp;Maryellen Giger,&nbsp;Christoph Wald,&nbsp;John Mongan,&nbsp;Tessa Cook,&nbsp;Neil Tenenholtz","doi":"10.1002/mp.17779","DOIUrl":"10.1002/mp.17779","url":null,"abstract":"<p>Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems (“clinical collaborators”), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task-specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.</p>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202632","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":"Treatment parameters consideration for universal range shifter-based multi-energy proton FLASH-RT","authors":"Yiling Zeng,&nbsp;Hong Quan,&nbsp;Qi Zhang,&nbsp;Wei Wang,&nbsp;Xu Liu,&nbsp;Bin Qin,&nbsp;Bo Pang,&nbsp;Muyu Liu,&nbsp;Shuoyan Chen,&nbsp;Kunyu Yang,&nbsp;Yu Chang,&nbsp;Zhiyong Yang","doi":"10.1002/mp.70039","DOIUrl":"10.1002/mp.70039","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Compared to conventional dose rate irradiation, ultra-high dose rate irradiation provides superior normal tissue sparing. Multi-energy proton beams combined with a universal range shifter (URS) and fast energy-switching gantry enable ultra-high dose rate delivery.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study investigates the effects of the URS, planning parameters, and patient selection on multi-energy Bragg peak (MEBP) proton FLASH radiotherapy (FLASH-RT) plans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Single-field plans were generated for water phantoms and a brain case, comparing beam setups with and without the URS. Planning parameters, including spot spacing, layer spacing, and beam orientation, were varied. The effects of fractional dose and target size were also assessed. Dose and FLASH-related metrics were analyzed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The use of a URS increased the spot size, which reduced the number of required spots and energy layers but also resulted in a broader penumbra, a prolonged distal falloff, and a higher D_mean in normal tissue. These effects became more pronounced with greater URS thickness. A spot spacing of 1.5 times the spot size (σ) and a layer spacing of 1.0 times the Bragg peak width (Proximal and Distal R80) improved V_40Gy/s, while effectively maintaining plan quality. Beam orientations with smaller field sizes increased V_40Gy/s. As the fractional dose increased, V_40Gy/s also increased, reaching saturation around 25 GyRBE. Additionally, V_40Gy/s improved with smaller target volumes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The URS has a significant impact on plan quality, requiring a balance between normal tissue sparing and the FLASH effect in MEBP planning. Although MEBP plan is suitable for treating tumors with complex shapes, careful selection of planning parameters is critical for achieving effective FLASH treatment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202575","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}