Medical physics最新文献

{"title":"Imaging system for real-time, full-field pulse-by-pulse surface dosimetry of UHDR electron beams","authors":"Megan Clark,&nbsp;Noah Daniel,&nbsp;Petr Bruza,&nbsp;Rongxiao Zhang,&nbsp;Lesley Jarvis,&nbsp;P. Jack Hoopes,&nbsp;David Gladstone","doi":"10.1002/mp.17784","DOIUrl":"10.1002/mp.17784","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The interest in ultra-high dose rate (UHDR) radiation therapy (RT) has grown due to its potential to spare normal tissue. However, clinical application is hindered by dosimetry challenges, as current irradiators and dosimeters are not designed for UHDR's high fluence. To ensure safe treatment and accurate dose delivery, real-time dose and dose rate quantification methods are essential.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We propose a novel scintillation imaging system for in vivo, pulse-by-pulse surface dose monitoring during delivery with a UHDR-capable Mobetron (IntraOp LLC Sunnyvale, CA, USA) system. This setup aims to measure entrance beam dose with high 2D spatial and temporal resolution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A modified collimating cone was 3D printed to house the imaging lens. The system featured a 90° sinuscope endoscope attached to a CMOS camera, was gated by the Mobetron's magnetron output signal, and captured light from a scintillator placed on the treatment surface. Three scintillator types were tested for their emission intensity and decay time. Dose and dose rate linearity studies were performed using various pulse lengths and repetition frequencies, respectively, and the imaging data were compared to an EDGE diode detector (SunNuclear Melbourne, FL, USA) and the Mobetron beam-current transformer (BCT) measurements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Dose (<i>R</i>²= 0.993) and dose rate (within 2%) were linear, and the temporal beam structure agreed with the diode and BCT data, as evident by the fact that it was successfully gated such that it captured each pulse during testing. Dose per pulse measurements agreed with diode and BCT data within 2.0 ± 1.2 cGy (0.6% ± 0.3%) and 2.5 ± 1.0 cGy (1.1% ± 0.4%), respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The developed imaging system met the criteria for measuring entrance beam dose with high spatial and temporal resolution, offering a promising in vivo dosimetry method for UHDR RT in preclinical and clinical trials.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"5026-5031"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766252","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":"Verification of dose and dose rate for quality assurance of spread-out-Bragg-peak proton FLASH radiotherapy using machine log files","authors":"Seyyedeh Azar Oliaei Motlagh,&nbsp;François Vander Stappen,&nbsp;Michele M. Kim,&nbsp;Rudi Labarbe,&nbsp;Lucian Hotoiu,&nbsp;Arnaud Pin,&nbsp;Rasmus Nilsson,&nbsp;Erik Traneus,&nbsp;Keith A. Cengel,&nbsp;Wei Zou,&nbsp;Boon-Keng Kevin Teo,&nbsp;Lei Dong,&nbsp;Eric S. Diffenderfer","doi":"10.1002/mp.17792","DOIUrl":"10.1002/mp.17792","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;Ultra-high dose rate radiotherapy elicits a biological effect (FLASH), which has been shown to reduce toxicity while maintaining tumor control in preclinical radiobiology experiments. FLASH depends on the dose rate, with evidence that higher dose rates drive increased normal tissue sparing. The pattern of dose delivery also has significance for conformal proton FLASH delivered via pencil beam scanning (PBS) given its unique spatio-temporal distribution of dose deposition.&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;In PBS, the machine-generated log file contains information on the spatio-temporal pattern of PBS delivery measured by the segmented ionization chambers in the treatment nozzle. The spot position and monitor unit (MU) obtained from log files have previously been used to reconstruct the treatment dose by Monte Carlo (MC) simulations. The incorporation of spot timing allows reconstruction of the 3D temporal dose distribution. The log-based dose and dose rate can have a role in quality assurance (QA) and FLASH treatment verification if the reconstruction can be shown to be accurate in spatial and temporal domains of dose deposition. Thus, the objective of this study is to validate the accuracy of dose rate reconstruction using input data from machine log files of PBS delivery. By analyzing the delivered spot timing, position, and MU extracted from the logs, we aim to evaluate the reliability and precision of the log data for dose and dose rate reconstruction.&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;FLASH PBS spread-out Bragg peak (SOBP) treatment fields were delivered using a cyclotron accelerated proton beam. This method involves a patient and field-specific conformal energy modulator (CEM) to achieve a SOBP at the tumor site. Log files record spot positions and the delivered MU with timing information at 250 µs resolution. To validate timing information, a 9.9 mm diameter parallel plate ionization chamber was positioned at various locations within the SOBP. An electrometer sampling at 20 kHz recorded the time-resolved ionization current collected by the ionization chamber. These measurements were used to determine spot dose, dose rate, duration, and transition times. Disparities between the measured and logged spot map MU and timing were determined. Dose average and PBS dose rates were compared between the measurement and log-based MC simulations.&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;There was a good agreement between the measured dwell time and transition time and the logged inform","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"5005-5016"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766257","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":"Impact of harmonization on predicting complications in head and neck cancer after radiotherapy using MRI radiomics and machine learning techniques","authors":"Benyamin Khajetash,&nbsp;Ghasem Hajianfar,&nbsp;Amin Talebi,&nbsp;Seid Rabi Mahdavi,&nbsp;Beth Ghavidel,&nbsp;Farshid Arbabi Kalati,&nbsp;Seyed Hadi Molana,&nbsp;Yang Lei,&nbsp;Meysam Tavakoli","doi":"10.1002/mp.17793","DOIUrl":"10.1002/mp.17793","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;Variations in medical images specific to individual scanners restrict the use of radiomics in both clinical practice and research. To create reproducible and generalizable radiomics-based models for outcome prediction and assessment, data harmonization is essential.&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 investigate the impact of harmonization in performance of machine learning-based radiomics model toward the prediction of radiotherapy-induced toxicity (early and late sticky saliva and xerostomia) in head and neck cancer (HNC) patients after radiation therapy using &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$T_1$&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;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$T_2$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-weighted magnetic resonance (MR) 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;A total of 85 HNC patients who underwent radiotherapy was studied. Radiomic features were extracted from &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$T_1$&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;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$T_2$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-weighted MR images with standardized protocols. Data harmonization was performed using ComBat algorithm to reduce inter-center variability. Besides imaging features, both dosimetric and demographic features were extracted and used in our model. Recursive feature elimination was employed as feature selection method to identify the most important variables. Ten classification algorithms, including eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), support vector machines (SVM), random forest (RF), k-nearest neighbor (KNN), Naive Bayes (NB), logistic regression (LR), and decision tree (DT), boosted generalized linear model (GLMB), and stack learning (SL) were utilized and compared to develop predictive models. This evaluation comparisons were performed before and after harmonization to demonstrate its significance.&lt;/p&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"5091-5103"},"PeriodicalIF":3.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756855","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":"Dosimetric impact of positional uncertainties and a robust optimization approach for rectal intensity-modulated brachytherapy","authors":"Björn Morén,&nbsp;Alana Thibodeau-Antonacci,&nbsp;Jonathan Kalinowski,&nbsp;Shirin A. Enger","doi":"10.1002/mp.17800","DOIUrl":"10.1002/mp.17800","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;Intensity-modulated brachytherapy (IMBT) employs rotating high-Z shields during treatment to decrease radiation in certain directions and conform the dose distribution to the target volume. Prototypes for dynamic IMBT have been proposed for prostate, cervical, and rectal cancer.&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;We considered two shielded applicators for IMBT rectal cancer treatment and investigated how rotational uncertainties in the shield angle and translational uncertainties in the source position affect plan evaluation criteria.&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 effect of rotational errors of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;$3^circ$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;$5^circ$&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;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;$10^circ$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and translational errors of 1, 2 and 3 mm on evaluation criteria were investigated for shields with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;180&lt;/mn&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;${rm 180}^circ$&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;msup&gt;\u0000 &lt;mn&gt;90&lt;/mn&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;${rm 90}^circ$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; emission windows. Further, a robust optimization approach based on quadratic penalties that includes scenarios with errors was proposed. The extent to which dosimetric effects of positional errors can be mitigated with this model was evaluated compared to a quadratic penalty model without scenarios with errors. A retrospective rectal cancer data set of ten patients was included in this study. Treatment planning was performed using the M","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"3528-3540"},"PeriodicalIF":3.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17800","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756819","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":"Improving realism in abdominal ultrasound simulation combining a segmentation-guided loss and polar coordinates training","authors":"Santiago Vitale,&nbsp;José Ignacio Orlando,&nbsp;Emmanuel Iarussi,&nbsp;Alejandro Díaz,&nbsp;Ignacio Larrabide","doi":"10.1002/mp.17801","DOIUrl":"10.1002/mp.17801","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;Ultrasound (US) simulation helps train physicians and medical students in image acquisition and interpretation, enabling safe practice of transducer manipulation and organ identification. Current simulators generate realistic images from reference scans. Although physics-based simulators provide real-time images, they lack sufficient realism, while recent deep learning-based models based on unpaired image-to-image translation improve realism but introduce anatomical inconsistencies.&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;We propose a novel framework to reduce hallucinations from generative adversarial networks (GANs) used on physics-based simulations, enhancing anatomical accuracy and realism in abdominal US simulation. Our method aims to produce anatomically consistent images free from artifacts within and outside the field of view (FoV).&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 introduce a segmentation-guided loss to enforce anatomical consistency by using a pre-trained Unet model that segments abdominal organs from physics-based simulated scans. Penalizing segmentation discrepancies before and after the translation cycle helps prevent unrealistic artifacts. Additionally, we propose training GANs on images in polar coordinates to limit the field of view to non-blank regions. We evaluated our approach on unpaired datasets comprising 617 real abdominal US images from a SonoSite-M turbo v1.3 scanner and 971 artificial scans from a ray-casting simulator. Data was partitioned at the patient level into training (70%), validation (10%), and testing (20%). Performance was quantitatively assessed with Frechet and Kernel Inception Distances (FID and KID), and organ-specific &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;χ&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;$chi ^2$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; histogram distances, reporting 95% confidence intervals. We compared our model against generative methods such as CUT, UVCGANv2, and UNSB, performing statistical analyses using Wilcoxon tests (FID and KID with Bonferroni-corrected &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;0.01&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$alpha = 0.01$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;χ&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4540-4556"},"PeriodicalIF":3.2,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756863","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":"Hybrid method for estimating lung ventilation from CT by combining intensity and motion information","authors":"Paris Tzitzimpasis,&nbsp;Mario Ries,&nbsp;Bas W. Raaymakers,&nbsp;Cornel Zachiu","doi":"10.1002/mp.17787","DOIUrl":"10.1002/mp.17787","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;Functional lung imaging modalities allow for capturing regional lung ventilation information. Computed Tomography based ventilation imaging (CTVI) has been proposed as a surrogate modality that relies on time-resolved anatomical data and image processing. However, generating accurate ventilation maps using solely computed tomography (CT) image information remains a challenging task, due to the need to derive functional information of ventilation from anatomical observations.&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;We introduce the hybrid estimation of computed tomography obtained respiratory function (HECTOR) method that consists of two components: a volume- and a density-based ventilation estimate. For the first component, a deformable image registration (DIR)—based solution for accurate volumetric CTVI generation is proposed, integrating the physical characteristics of the lung deformations in its design. For the second component, an already established air-tissue density model is used. Furthermore, a novel method is developed for combining the two components.&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 proposed method consists of four principal steps: (1) Application of a specially tailored DIR algorithm to estimate respiratory motion between inhale and exhale phases. (2) Conversion of the motion information to volumetric change maps using a variation of the Jacobian determinant method. (3) Computation of a HU-based method that estimates the local product of air-tissue densities. (4) Combination of the metrics estimated in steps 2 and 3 by means of a smooth minimum function.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;The proposed approach is validated using the publicly available VAMPIRE dataset consisting of two subgroups: 25 subjects scanned with Galligas 4DPET/CT and 21 subjects scanned with DTPA-SPECT. Another dataset of 18 patients available at The Cancer Imaging Archive (TCIA) was used for further validation. All datasets contain inhale/exhale CT scans paired with ground-truth ventilation images (RefVIs). The CTVIs generated by the proposed HECTOR method were tested against the RefVIs using the Spearman correlation coefficient and Dice overlap of low- and high-function lung (DSC-low and DSC-high, respectively).&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 proposed method achieved mean Spearman, DSC-high and DSC-low coefficients of 0.62, 0.55, and 0.59 on the Galligas PET subgroup and 0.49,0,48, and 0.50 on the DTPA-SPECT subgroup of the VAMPIRE dataset. This performance was better than th","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4528-4539"},"PeriodicalIF":3.2,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756822","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":"Proton dose calculation with transformer: Transforming spot map to dose","authors":"Xueyan Tang,&nbsp;Hok Wan Chan Tseung,&nbsp;Mark D. Pepin,&nbsp;Jed E. Johnson,&nbsp;Doug J. Moseley,&nbsp;David M. Routman,&nbsp;Jing Qian","doi":"10.1002/mp.17794","DOIUrl":"10.1002/mp.17794","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;Conventional proton dose calculation methods are either time- and resource-intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade-off between computational efficiency and accuracy highlights the need for improved dose calculation approaches in clinical settings.&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 a deep-learning-based model that calculates dose-to-water (&lt;i&gt;D&lt;/i&gt;&lt;sub&gt;W&lt;/sub&gt;) and dose-to-medium (&lt;i&gt;D&lt;/i&gt;&lt;sub&gt;M&lt;/sub&gt;) using patient anatomy and proton spot map (PSM), achieving approaching MC-level accuracy with significantly reduced computation time. Additionally, the study seeks to generalize the model to different treatment sites using transfer learning.&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 SwinUNetr model was developed using 259 four-field prostate proton stereotactic body radiation therapy (SBRT) plans to calculate patient-specific &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;W&lt;/sub&gt; and &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;M&lt;/sub&gt; distributions from CT and projected PSM (PPSM). The PPSM was created by projecting PSM into the CT scans using spot coordinates, stopping power ratio, beam divergence, and water-equivalent thickness. Fine-tuning was then performed for the central nervous system (CNS) site using 84 CNS plans. The model's accuracy was evaluated against MC simulation benchmarks using mean absolute error (MAE), gamma analysis (2% local dose difference, 2-mm distance-to-agreement, 10% low dose threshold), and relevant clinical indices on the test dataset.&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 trained model achieved a single-field dose calculation time of 0.07 s on a Nvidia-A100 GPU, over 100 times faster than MC simulators. For the prostate site, the best-performing model showed an average MAE of 0.26 ± 0.17 Gy and a gamma index of 92.2% ± 3.1% in dose regions above 10% of the maximum dose for &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;W&lt;/sub&gt; calculations, and an MAE of 0.30 ± 0.19 Gy with a gamma index of 89.7% ± 3.9% for &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;M&lt;/sub&gt; calculations. After transfer learning for CNS plans, the model achieved an MAE of 0.49 ± 0.24 Gy and a gamma index of 90.1% ± 2.7% for &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;W&lt;/sub&gt; computations, and an MAE of 0.47 ± 0.25 Gy with a gamma index of 85.4% ± 7.1% for &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;M&lt;/sub&gt; computations.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The SwinUNetr model provides an efficient and accurate method for computing dose distributions in proton therapy. It also opens the possibility of reverse-engineering PSM from &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;W&lt;/sub&gt;, potentially","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4941-4952"},"PeriodicalIF":3.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744656","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":"Characterization and inter-scanner reproducibility of geometric distortion on a small footprint, high-performance, head-specific 0.5 T scanner","authors":"Curtis N. Wiens,&nbsp;Chad T. Harris,&nbsp;Ian R. O. Connell","doi":"10.1002/mp.17789","DOIUrl":"10.1002/mp.17789","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Magnetic resonance imaging (MRI) offers superior soft tissue contrast and essential imaging capabilities for modern medicine. MRI is increasingly being used in applications that require a high degree of spatial fidelity; however, distortions are a well-known limitation of the modality. The mid-field (0.3 T ≤ B₀ &lt; 1 T) has advantages in this respect due to being less susceptible to patient-induced distortions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this work was to characterize the geometric fidelity of a short-bore, head-specific, 0.5T MRI system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Assessment of spatial fidelity was performed using a 3D gradient recalled echo (GRE) acquisition on a commercial distortion phantom using the validated distortion analysis software provided. B₀-induced distortions were measured using a 3D field map. Inter-scanner reproducibility was assessed across four distinct systems of identical make and model, while intra-scanner repeatability was assessed at one site over six repeat measurements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Inter-scanner reproducibility measured an average 95th percentile distortion over 100 and 180 mm DSV of 0.15 ± 0.03  and 0.33 ± 0.05 mm. Average 95th percentile distortions due to B₀ field inhomogeneities over 100 and 180 mm DSV were 0.02 ± 0.01  and 0.07 ± 0.02 mm. Intra-scanner repeatability measured the uncertainty in distortion values to be 0.020 ± 0.005 mm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The total residual distortions measured in this phantom study were less than half the recommended value required for radiosurgery and significantly better than data published from other MR systems. This demonstrates that in addition to the compact footprint of the Synaptive 0.5T scanner, it exceeds current standards for geometric accuracy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4598-4604"},"PeriodicalIF":3.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744673","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":"Postal dosimetry audit for scanning proton beam using radiophotoluminescence glass dosimeter: A multicenter pilot study","authors":"Keisuke Yasui,&nbsp;Miuna Hayashi,&nbsp;Shiryu Otsuka,&nbsp;Toshiyuki Toshito,&nbsp;Chihiro Omachi,&nbsp;Masaya Ichihara,&nbsp;Riki Oshika,&nbsp;Yuki Tominaga,&nbsp;Hiromi Baba,&nbsp;Hidetoshi Shimizu,&nbsp;Naoki Hayashi","doi":"10.1002/mp.17790","DOIUrl":"10.1002/mp.17790","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Accurate dosimetry is important in radiotherapy, and all equipment used for radiotherapy shoud be audited by an independent external dose audit. Radiophotoluminescence glass dosimeter (RPLD) has excellent characteristics and is widely used for postal dose audit; however, postal dose audit for proton therapy using RPLD has not been established.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to develop a postal dose audit procedure for scanning proton beams using RPLD, estimate uncertainties, and conduct a multicenter pilot study to validate the methodology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A postal toolkit was developed and a postal dose audit procedure for RPLD measurements of scanning proton beams was established in cooperation with several facilities that employ various accelerators, irradiation equipment, and treatment planning systems (TPS) for clinical use. Based on basic and previous studies, an uncertainty budget was developed for estimating relative uncertainty and pilot studies were conducted at each site. A method for postal dose audits was developed in a multicenter collaboration to develop an approach suitable for implementation across multiple facilities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The relative response of 60 RPLDs for scanning proton beam examined in this study was 1.00 ± 1.28% mean ± standard deviation. The combined relative standard uncertainty of postal dosimetry for scanning proton beams using the RPLD was 2.97% (k = 1). Under the reference condition, the maximum differences between the ionization chamber measurement (IC) and TPS, RPLD and TPS, and RPLD and IC were 0.97, 1.88, and 2.12%, respectively. The maximum differences between the RPLD and ionization chamber for plateau measurements at 3 cm depth using single-energy and non-reference conditions were 11.31 and 4.02%, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We established a procedure for the postal dose audits of proton beams using RPLD and presented the results of a multicenter pilot study. By standardizing the reference conditions, the dosimetry uncertainty was estimated at 2.92%. The results demonstrated the feasibility of performing an independent third-party dose audit of scanning proton beams using RPLD, and for such postal dose audits for proton beams, the irradiation conditions should be standardized to reduce uncertainties. These results are expected to contribute to the development of proton beams.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4996-5004"},"PeriodicalIF":3.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744674","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":"A segment anything model-guided and match-based semi-supervised segmentation framework for medical imaging","authors":"Guoping Xu,&nbsp;Xiaoxue Qian,&nbsp;Hua-Chieh Shao,&nbsp;Jax Luo,&nbsp;Weiguo Lu,&nbsp;You Zhang","doi":"10.1002/mp.17785","DOIUrl":"10.1002/mp.17785","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;Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. The Match-based framework, by using the consistency constraint of segmentation results from different models/augmented label-less inputs, is found effective in semi-supervised learning. This approach, however, is challenged by the low quality of pseudo-labels generated as intermediate products for training the network, due to the lack of the ‘‘ground-truth’’ reference.&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 leverage the foundation model, segment anything model (SAM), to assist unsupervised learning of Match-based frameworks. Trained with an extremely large dataset, SAM-based methods generalize better than traditional models to various imaging domains, allow it to serve as an assistant to Match-based frameworks to improve the quality of intermediate pseudo-labels for semi-supervised learning.&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 propose SAM-Match, a SAM-guided and Match-based framework for semi-supervised medical image segmentation. Our approach involves two main steps: First, we use pretrained Match-based models to extract high-confidence predictions for prompt generation. Second, these prompts and unlabeled images are input into a fine-tuned SAM-based method to produce high-quality masks as pseudo-labels. And the refined pseudo-labels are further fed back to train the Match-based framework. SAM-Match can be trained in an end-to-end manner, facilitating interactions between the SAM- and Match-based models.&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;SAM-Match demonstrates robust performance across multiple medical imaging datasets, including the ACDC cardiac MRI dataset, the BUSI breast ultrasound dataset, and an in-house liver MRI dataset (MRLiver). We partitioned the datasets into training, validation, and test sets (70%, 10%, and 20% for ACDC; 60%, 9%, and 31% for BUSI; and 62%, 12%, and 25% for MRLiver). On ACDC, with only 3 labeled cases, we achieved a Dice score of 89.36% ± 0.06% on 20 test cases. For BUSI, using just 30 labeled samples for training, we attained a Dice score of 59.35% ± 0.12% on 170 test samples. On MRLiver, training with only 3 labeled cases resulted in a Dice score of 80.04% ± 0.11% on 12 test scans. Wilcoxon signed-rank tests with Bonferroni corrections between the SAM-Match framework and the other comparison methods further demonstrated the statistical significance of SAM-Match's i","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 6","pages":"4513-4527"},"PeriodicalIF":3.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744672","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}