Medical physics最新文献

{"title":"A machine learning based dual-energy CT elemental decomposition method and its physical-biological impacts on carbon ion therapy","authors":"Yan Li, Weiguang Li, Chao Yang, Shutong Yu, Cheng Chang, Chong Xu, Mingqing Wang, Kai-Wen Li, Li-Sheng Geng, Yibao Zhang","doi":"10.1002/mp.18082","DOIUrl":"https://doi.org/10.1002/mp.18082","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Dual-energy computed tomography (DECT) enhances material differentiation by leveraging energy-dependent attenuation properties particularly for carbon ion therapy. Accurate estimation of tissue elemental composition via DECT can improve quantification of physical and biological doses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study proposed a novel machine-learning-based DECT (ML-DECT) method to predict the physical density and mass ratios of H, C, N, O, P, and Ca. The physical and biological impacts on carbon ion therapy was also investigated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Taking DECT-derived CT numbers as inputs, a fully connected neural network was employed to predict the physical density or the elemental mass ratio. The training and testing utilized a dataset of 85 biological tissues with data augmentation. The prediction accuracy and noise analysis were compared against the parameterization DECT (PA-DECT) and SECT methods. By applying the proposed method on the DECT images of 10 head-and-neck patients, the physical and biological doses as well as the linear energy transfer (LET) were calculated for a set of carbon ion pencil beams using Monte-Carlo simulations. Patient-based results were compared with the PA-DECT method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The ML-DECT method yielded <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <mo>=</mo>\u0000 <mn>0.9996</mn>\u0000 </mrow>\u0000 <annotation>${R}^2 = 0.9996$</annotation>\u0000 </semantics></math> for physical density and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <mo>=</mo>\u0000 <mn>0.8338</mn>\u0000 <mo>∼</mo>\u0000 <mn>0.9997</mn>\u0000 </mrow>\u0000 <annotation>${R}^2 = 0.8338sim 0.9997$</annotation>\u0000 </semantics></math> for the six elemental mass ratios across 85 materials. Compared to the PA-DECT and SECT methods, the accuracy was improved by over 20% and 50%; the noise robustness was improved by over three times and up to 25%, respectively. In the patient dose evaluation, the ML-DECT method yielded comparable physical and biological doses, yet up to ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021987","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 generative adversarial network to improve integrated mode proton imaging resolution using paired proton–carbon data","authors":"Mikaël Simard,&nbsp;Ryan Fullarton,&nbsp;Lennart Volz,&nbsp;Christoph Schuy,&nbsp;Savanna Chung,&nbsp;Colin Baker,&nbsp;Christian Graeff,&nbsp;Charles-Antoine Collins Fekete","doi":"10.1002/mp.18081","DOIUrl":"https://doi.org/10.1002/mp.18081","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Integrated mode proton imaging is a clinically accessible method for proton radiographs (pRads), but its spatial resolution is limited by multiple Coulomb scattering (MCS). As the amplitude of MCS decreases with increasing particle charge, heavier ions such as carbon ions produce radiographs with better resolution (cRads). Improving image resolution of pRads may thus be achieved by transferring individual proton pencil beam images to the equivalent carbon ion data using a trained image translation network. The approach can be interpreted as applying a data-driven deconvolution operation with a spatially variant point spread function.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Propose a deep learning framework based on paired proton–carbon data to increase the resolution of integrated mode pRads.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A conditional generative adversarial network, Proton2Carbon, was developed to translate proton pencil beam images into synthetic carbon ion beam images. The model was trained on 547 224 paired proton–carbon images acquired with a scintillation detector at the Marburg Ion Therapy Centre. Image reconstruction was performed using a 2D lateral method, and the model was evaluated on internal and external datasets for spatial resolution, using custom 3D-printed line pair modules.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The Proton2Carbon model improved the spatial resolution of pRads from 1.7 to 2.7 lp/cm on internal data and to 2.3 lp/cm on external data, demonstrating generalizability. Water equivalent thickness accuracy remained consistent with pRads and cRads. Evaluation on an anthropomorphic head phantom showed enhanced structural clarity, though some increased noise was observed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study demonstrates that deep learning can enhance pRad image quality by leveraging paired proton–carbon data. Proton2Carbon can be integrated into existing imaging workflows to improve clinical and research applications of proton radiography. To facilitate further research, the full dataset used to train Proton2Carbon is publicly released and available at https://zenodo.org/records/14945165.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145022047","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":"Development and characterization of a prototype selenium-75 high dose rate brachytherapy source","authors":"Jonathan Kalinowski,&nbsp;Oren Tal,&nbsp;Jake Reid,&nbsp;John Munro III,&nbsp;Matthew Moran,&nbsp;Andrea Armstrong,&nbsp;Shirin A. Enger","doi":"10.1002/mp.18088","DOIUrl":"https://doi.org/10.1002/mp.18088","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;&lt;sup&gt;75&lt;/sup&gt;Se (&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;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$t_{1/2}$&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;mo&gt;≈&lt;/mo&gt;\u0000 &lt;annotation&gt;$approx$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;120 days, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;E&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;γ&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mtext&gt;avg&lt;/mtext&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$E_{gamma,text{avg}}$&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;mo&gt;≈&lt;/mo&gt;\u0000 &lt;annotation&gt;$approx$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;215 keV) offers advantages over &lt;sup&gt;192&lt;/sup&gt;Ir (&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;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$t_{1/2}$&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;mo&gt;≈&lt;/mo&gt;\u0000 &lt;annotation&gt;$approx$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;74 days, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;E&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;γ&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mtext&gt;avg&lt;/mtext&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$E_{gamma,text{avg}}$&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;mo&gt;≈&lt;/mo&gt;\u0000 &lt;annotation&gt;$approx$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;360 keV) as a high dose rate brachytherapy source due to its lower gamma energy and longer half-life. Despite its widespread use in industrial gamma radiography, a &lt;sup&gt;75&lt;/sup&gt;Se brachytherapy source has yet","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145022205","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":"LET measurements in proton and helium-ion beams of therapeutic energies using a silicon pixel detector towards a tool for quality assurance","authors":"Yasmin Hamad,&nbsp;Ferisya Kusuma Sari,&nbsp;Renato Félix-Bautista,&nbsp;Mária Martišíková,&nbsp;Andrea Mairani,&nbsp;Tim Gehrke","doi":"10.1002/mp.18085","DOIUrl":"https://doi.org/10.1002/mp.18085","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;As advanced treatment plans increasingly include optimizing both dose and linear energy transfer (LET), there is a growing demand for tools to measure LET in clinical settings. Although various detection systems have been investigated in this pursuit, the scarcity of detectors capable of providing per-ion data for a fast and streamlined verification of LET distributions remains an issue. Silicon pixel detector technology bridges this gap by enabling rapid tracking of single-ion energy 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;This study proposes a methodology for assessing LET and relative biological effectiveness (RBE) in mixed radiation fields produced by clinical proton and helium ion beams, using a hybrid silicon pixel detector equipped with a Timepix3 chip.&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 Timepix3 detector was placed behind PMMA slabs of different thicknesses and exposed to initially monoenergetic proton and helium-ion beams. The detector featured a 300 µm-thick silicon sensor operated in partial depletion. Silicon-based LET spectra were derived from single-ion deposited energy across the sensor and subsequently converted to water-equivalent spectra. Track- and dose-averaged LET (&lt;i&gt;LET&lt;sub&gt;t&lt;/sub&gt;&lt;/i&gt; and &lt;i&gt;LET&lt;sub&gt;d&lt;/sub&gt;&lt;/i&gt;) were calculated from these spectra. LET measurements were used as input to estimate the RBE via the modified microdosimetric kinetic model (mMKM) assuming an (α/β)&lt;i&gt;&lt;sub&gt;γ&lt;/sub&gt;&lt;/i&gt; value of 2 Gy. Measurements were compared with simulations performed using the FLUKA Monte Carlo code. Energy deposition spectra, &lt;i&gt;LET&lt;sub&gt;t&lt;/sub&gt;&lt;/i&gt; and &lt;i&gt;LET&lt;sub&gt;d&lt;/sub&gt;&lt;/i&gt; values were simulated at various depths in PMMA for the radiation fields used, by considering the contribution from the secondary particles generated in the ion interaction processes as well.&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;Energy deposition spectra were validated against Monte Carlo simulations, showing good agreement in both spectral shapes and positions. However, a depth uncertainty of less than 1 mm and other potential differences between measurements and simulations led to deviations, particularly in the distal region of the Bragg curve. Relative differences of &lt;i&gt;LET&lt;sub&gt;d&lt;/sub&gt;&lt;/i&gt; between measurements and simulations were within 3% for protons and 10% for helium ions upstream of the Bragg curves. Notably, larger discrepancies were observed in the distal part of the Bragg curve, with maximum relative differences of 7% for protons and 17% for helium ions. Average differ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935012","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":"Conventional, time-dependent, and continuous-time random-walk diffusion-weighted imaging models in microstructural characterization of breast lesions at 3.0T: A prospective analysis","authors":"Xue Li,&nbsp;Yinqiao Yi,&nbsp;Yanglei Wu,&nbsp;Bin Hua,&nbsp;Lei Jiang,&nbsp;Min Chen","doi":"10.1002/mp.17960","DOIUrl":"https://doi.org/10.1002/mp.17960","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;Advanced diffusion models have been introduced to improve characterization of tissue microstructure in breast cancer assessment.&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 aimed to evaluate the diagnostic utility of monoexponential apparent diffusion coefficient (ADC), time-dependent diffusion magnetic resonance imaging (td-dMRI), and the Continuous-Time Random-Walk (CTRW) diffusion model for differentiating breast lesions and predicting Ki-67 expression levels.&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;Fifty-three consecutive patients with suspected breast lesions undergoing preoperative MRI were enrolled in this prospective investigation. Each participant underwent conventional diffusion-weighted imaging (DWI), CTRW, and td-dMRI acquisition. From conventional DWI, ADC&lt;sub&gt;mean&lt;/sub&gt;, ADC&lt;sub&gt;min&lt;/sub&gt;, and ADC&lt;sub&gt;max&lt;/sub&gt; were extracted from two-dimensional lesion regions of interest, and the intralesional ADC difference (ADC&lt;sub&gt;max&lt;/sub&gt; − ADC&lt;sub&gt;min&lt;/sub&gt;) was computed. CTRW analysis involved whole-lesion histograms to quantify temporal heterogeneity (α), spatial heterogeneity (β), and the anomalous diffusion coefficient (D). td-dMRI data were fitted using the JOINT model to derive five microstructural parameters, with PGSE&lt;sub&gt;50ms&lt;/sub&gt; also obtained. Group comparisons of diffusion parameters between benign and malignant lesions were performed using Mann–Whitney U tests, followed by correlation analyses with Ki-67. Bonferroni correction was applied to account for multiple testing, with &lt;i&gt;p &lt;&lt;/i&gt; 0.05 indicating statistical significance. Logistic regression was employed to combine significant parameters, and diagnostic performance was assessed via receiver operating characteristic (ROC) analysis.&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 td-dMRI-derived &lt;i&gt;f&lt;/i&gt;&lt;sub&gt;in&lt;/sub&gt; and cellularity, alongside various CTRW-based histogram parameters, demonstrated statistically significant distinctions between benign and malignant breast lesions (all adjusted &lt;i&gt;p &lt;&lt;/i&gt; 0.05, Bonferroni correction). Among all evaluated models, the combined CTRW metrics yielded the highest area under the ROC curve (AUC) (0.975), indicating markedly improved diagnostic efficacy compared to conventional DWI (all &lt;i&gt;p &lt;&lt;/i&gt; 0.05). Diffusion metrics generated from ADC, α, and td-dMRI maps were significantly associated with Ki-67 expression (&lt;i&gt;ρ&lt;/i&gt; = 0.39–0.62, all &lt;i&gt;p &lt;&lt;/i&gt; 0.05).&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;Diffusion parameters derived from conventional DWI, td-dMRI, and","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990649","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":"Software Article: A generalized cascaded linear system model implementation for x-ray detectors","authors":"Gustavo Pacheco,&nbsp;Juan J. Pautasso,&nbsp;Koen Michielsen,&nbsp;Ioannis Sechopoulos","doi":"10.1002/mp.18079","DOIUrl":"https://doi.org/10.1002/mp.18079","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Cascaded linear models are widely used for the development and optimization of x-ray imaging systems, yet no publicly available Python implementation currently exists. We introduce CASYMIR, a flexible and open-source Python package capable of modeling direct and indirect-conversion x-ray imaging detectors under various acquisition conditions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We employed a modular software design with generalized frequency-domain expressions for each process in the detection chain, which can be implemented as serial or parallel blocks. The gain factors and other parameters are derived from the detector's characteristics, system geometry, and incident x-ray spectra, all of which can be specified by the user. The signal reaching the detector is propagated throughout the detection stages by applying these process blocks, enabling the computation of the Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) at any stage of the model.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Validation&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Our implementation was experimentally validated using two commercial x-ray detectors: a flat-panel a-Se detector for digital mammography and digital breast tomosynthesis, and a flat-panel scintillator (CsI) detector for dedicated breast CT. The modeled MTF had root-mean-square (RMS) percent errors below 6% for the a-Se detector, while the normalized RMS error for the NNPS was below 3%. For the CsI detector, the RMS percent error in the MTF was 5.4%, and the normalized RMS error for the NNPS was 5.8%.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Usage notes&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The CASYMIR Python package can be downloaded from https://github.com/radboud-axti/casymir_public, and it includes a standalone executable script suitable for modeling common commercial systems, along with an extensive README file and example files.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Potential applications&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;CASYMIR is available as an open-source Python package under the MIT license. Given its modular and flexible structure, it can be easily modified and integrated into other simulation/virtual clinical trial pipelines where information about the detector's spatial resolution and noise performance is needed. The standalone version of CASYMIR may be particularly useful for running batch simulations with varying acquisition and system parameters, making it ideal for optimizing system design and acquisition techniques. Furthermore, given the package's modul","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935259","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":"Automatic trending and analysis of SPECT quality assurance with artificial intelligence optical character recognition","authors":"Shanli Ding,&nbsp;Rachel M. Barbee,&nbsp;Osama Mawlawi,&nbsp;Tinsu Pan","doi":"10.1002/mp.18083","DOIUrl":"https://doi.org/10.1002/mp.18083","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;To guarantee high-quality patient scans, thorough quality assurance (QA) of SPECT or gamma cameras, including performance, review, and documentation, 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;We developed a novel Nuclear Medicine Quality Assurance server (NMQA) with an AI deep learning (AIDL) optical character recognition (OCR) system to automate QA data retrieval and review from SPECT and gamma cameras. The system extracts and compares daily and weekly QA data against specifications. Our goal was to improve the efficiency of QA reviews and facilitate trending, storage, and auditing of QA data across our large hospital network.&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 NMQA Server was implemented in a Linux system using open-source Python as the programming language, DICOM tool kit DCMTK for query of QA data, and Pydicom for managing DICOM images and Structured Query Language (SQL) for interacting with a relational MySQL database. The MySQL database stores numerical results for intrinsic and extrinsic floods, MHR, and COR, along with pointers to the image database facilitating trending analysis of numerical values and flood data evaluation. It also streamlines the review through the server's web interface, accessible on iPhones, iPads, and computers. The AIDL OCR is structured into three stages: feature extraction, sequence labeling, and transcription. The OCR comprises two steps: region of interest (ROI) extraction and character recognition. The AIDL OCR was benchmarked for both accuracy and speed against four common OCRs of Tesseract, OCRopus, PhotoOCR, and EasyOCR on a QA dataset, consisting of 60 flood and 6 COR images without post-processing, and evaluated for accuracy on 3459 flood-scans with post-processing.&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 new NMQA server can automatically query QA data, avoid the frequent mistake of typographical errors in naming the QA data, extract the numerical values of the QA data, and build a QA database for trending and analysis of the QA data. It takes about 3 min to complete a query of QA data from all 14 scanners and subsequent postprocessing. The web design facilitated review of flood images over days. The time to review the QA data on PACS without the NMQA server was about 60 min and has been reduced to several minutes using the new NMQA server web page on iPhones, iPads, or computers. The AIDL OCR outperformed Tesseract, OCRopus, PhotoOCR, and EasyOCR in speed and accuracy, maintaining CPU-friendly performance with a processing speed of just 0.3 s per image and accuracy of 93.53%. The AIDL OCR achieved an accur","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 8","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935007","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":"Liver fat volume fraction assessment in medium and large anthropomorphic phantoms–dual energy virtual monochromatic imaging versus single-energy CT","authors":"Yifang Zhou,&nbsp;Xinhua Li","doi":"10.1002/mp.18105","DOIUrl":"https://doi.org/10.1002/mp.18105","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;Fat volume fraction (FVF) is an important biomarker for non-alcoholic fatty liver disease. However, current CT-based FVF quantification methods lack sufficient accuracy, particularly at lower FVF values.&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 aimed to analyze the relationship between FVF and Hounsfield units (HU) in unenhanced fatty lesions and identify optimal settings to minimize FVF quantification errors by comparing virtual monochromatic imaging (VMI) from dual-energy CT (DECT) with single-energy CT (SECT) across different patient sizes.&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;Six fatty lesions (5%–40% FVF) were embedded in an anthropomorphic liver within a medium-sized abdomen-pelvis phantom (25 cm×32.5 cm). DECT acquisitions were conducted at CTDI&lt;sub&gt;vol&lt;/sub&gt; of 14.5 mGy using both fast kV-switching (FKVS) and dual-source CT (DSCT), producing VMI images from 40 to 140 keV. HU values were measured across all VMI energies for each lesion in three repeated acquisitions. The measured HU values were correlated with the known FVF. For comparison, repeated single-energy images were also acquired at 120 kV with the same dose, and a similar analysis was performed. To study the impact of the patient size, a large phantom (31 cm×39 cm) consisted of an additional soft-tissue equivalent layer to the medium-size phantom was scanned on the FKVS CT with noise matched CTDI&lt;sub&gt;vol&lt;/sub&gt; = 21 mGy using DECT and SECT.&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;It was found that the validity of the linear relation for FVF-HU is x-ray beam energy and VMI energy-dependent. For the medium-sized phantom, the root-mean-square (RMS) FVF estimation errors from the linearity assumption were slightly higher than 9% with the SECT at 120 kV and CTDI&lt;sub&gt;vol&lt;/sub&gt; = 14.5 mGy on both units, and the corresponding maximum individual FVF errors were 16.5%–23% at FVF ≤ 10%. With the FKVS CT, four VMI settings resulted in lower RMS errors than SECT with the smallest RMS. error (∼5%) at 90 and 100 keV, where the maximum individual FVF errors were approximately 10% occurred at FVF ≤ 10%. For the DSCT with spectra 80/Sn 150 kV, five VMI settings resulted in smaller RMS errors than 9%, with the lowest RMS error (∼2.5%) at 120 and 130 keV, where the maximum individual FVF errors ≤4.4% occurred at 30% FVF, respectively. For the large phantom, however, the linear model at single energy of 140 kV resulted in the lowest RMS error of 9.2% with the maximum individual FVF error of 20% at 5% FVF, while the smallest RMS error from VMI was 15.3% with the maximum individual FVF error of 27% at 10% FVF.&lt;/p&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935164","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 novel federated learning framework for medical imaging: Resource-efficient approach combining PCA with early stopping","authors":"Negin Piran Nanekaran,&nbsp;Eranga Ukwatta","doi":"10.1002/mp.18064","DOIUrl":"https://doi.org/10.1002/mp.18064","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;Federated learning (FL) facilitates collaborative model training across multiple institutions while preserving privacy by avoiding the sharing of raw data, a critical consideration in medical imaging applications. Despite its potential, FL faces challenges such as high-dimensional data, heterogeneity among datasets from different centers, and resource constraints, which limit its efficiency and effectiveness in healthcare 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 present a novel adaptive FL framework to address the challenges of data heterogeneity and resource constraints in medical imaging. The proposed framework is designed to optimize computational efficiency, enhance training processes, improve model performance, and ensure robustness against non-independent and identically distributed (non-IID) data across decentralized data sources.&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 adaptive FL framework addresses the challenges of high-dimensional data and heterogeneity in nonuniform and decentralized data sources through a key innovation. First, Federated incremental principal component analysis (FIPCA) achieves privacy-preserving dimensionality reduction by aggregating local scatter matrices and means from participating centers, enabling the computation of a global PCA model. This process ensures data alignment across centers, mitigates heterogeneity, and significantly reduces computational complexity. We evaluated the framework's ability to generalize across institutions in a cross-site classification task distinguishing clinically significant prostate cancer (csPCa) from non-csPCa. This assessment used 1500 T2-weighted (T2W) prostate MRI images from three institutions, where two centers (800 + 350 cases) were used for training and validation, and one center (350 cases) served as an independent test site.&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 significantly reduced the number of global training rounds from 200 to 38, achieving a 98% reduction in energy consumption compared to the standard FedAvg algorithm. The effective use of FIPCA for dimensionality reduction enhanced generalizability, while adaptive early stopping prevented overfitting, leading to an improvement in model performance, with the area under the curve (AUC) on the unseen test center increasing from 0.68 to 0.73 (95 % CI 0.70 – 0.77) on the test center's data. Additionally, the method demonstrated improved sensitivity and specificity, indicating superior classification","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 8","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935351","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":"Effect of immobilization mask and beam energy on dose coverage to small joints in treating osteoarthritis with low-dose radiation therapy","authors":"George X. Ding,&nbsp;Kenneth L. Homann,&nbsp;Eric T. Shinohara","doi":"10.1002/mp.18099","DOIUrl":"https://doi.org/10.1002/mp.18099","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Osteoarthritis (OA) is the most common form of arthritis, affecting over 32 million Americans. Low dose radiation therapy (LDRT) is being used to treat OA, including small joints. Treatment energies recommended include both orthovoltage and 6 MV photons. This study evaluates treatment plan accuracy of small joints using a commercial treatment planning system (TPS) when 6 MV is used. The effect of bolus and immobilization mask on target dose coverage and the use of 2.5 MV beams are also studied.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Monte Carlo calculated dose distributions were used to evaluate the dose calculation accuracy of small joints by the Varian Eclipse system (AAA V.16) for one patient. The CT based dose calculations with- and without an Aquaplast immobilization mask using 6 MV and 2.5 MV beams were compared. The target dose coverages were analyzed using a dose volume histogram (DVH). The effect of the Aquaplast mask on target dose coverage was evaluated. The doses calculated by Monte Carlo (MC) were regarded as the Gold Standard.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The dose calculated by the Eclipse system significantly underestimated D₉₅ target coverage by up to 21% of the prescribed dose. D₉₅ was 92.9%, 91.7% and 89.6% of prescribed dose with 1 cm bolus, with a custom Aquaplast mask, and without a custom Aquaplast mask based on MC calculations, respectively, as compared to 86.8%, 83.2% and 73.9% when using Eclipse.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Eclipse calculations are less accurate, and underestimate D₉₅ target dose by 7% even with bolus. When Monte Carlo is not available, prescribing to the D50 in Eclipse can lead to an actual D₉₅ coverage of &gt;90%. The immobilization mask provides adequate buildup for 6 MV beam. To obtain the full benefit of lower-energy beams the 2.5 MV-flattened beam provided the best dose coverage regardless of the use of a mask when treating small joints.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.18099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935356","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}