Medical physics最新文献

{"title":"Cover","authors":"","doi":"10.1002/mp.17204","DOIUrl":"https://doi.org/10.1002/mp.17204","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"C1"},"PeriodicalIF":3.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784282","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":"List of Advertisers","authors":"","doi":"10.1002/mp.17205","DOIUrl":"https://doi.org/10.1002/mp.17205","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2697"},"PeriodicalIF":3.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784305","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":"Cover","authors":"","doi":"10.1002/mp.17201","DOIUrl":"https://doi.org/10.1002/mp.17201","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"C1"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554433","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":"List of Advertisers","authors":"","doi":"10.1002/mp.17202","DOIUrl":"https://doi.org/10.1002/mp.17202","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1969"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554663","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":"Breast cancer detection from ultrasound computed tomography imaging using radiomic analysis: in silico trial","authors":"Andres Vargas,&nbsp;Nicole Hernandez,&nbsp;Ana B. Ramirez,&nbsp;Said Pertuz","doi":"10.1002/mp.17710","DOIUrl":"10.1002/mp.17710","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Ultrasound computed tomography (USCT) is an imaging modality currently under development for its clinical use in breast imaging. In order to justify clinical trials on imaging prototypes, further research is required to investigate uses and limitations of USCT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We investigate the potential of USCT for the detection of breast lesions through the computerized analysis of speed-of-sound (SOS) images of the breast.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted an in silico study with a set of 116 virtual breast phantoms (VBPs). We simulated US acquisition and reconstructed 2D SOS slices of the breast via the full waveform inversion (FWI) technique. Subsequently, we conducted breast lesion detection based on computerized texture features (i.e., radiomic features) of the SOS slices. We compare the performance in cancer detection against radiomic analysis of mammograms in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve with 95% confidence intervals estimated using five-fold cross-validation. Statistical analysis involved the Wilcoxon rank-sum test to evaluate significant differences in detection scores, with a significance level of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>&lt;</mo>\u0000 <mn>0.05</mn>\u0000 </mrow>\u0000 <annotation>$p&lt;0.05$</annotation>\u0000 </semantics></math>. AUCs were compared using DeLong's test, and the significance level was adjusted with Bonferroni's correction to account for multiple comparisons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The AUC for lesion detection from reconstructed SOS images and mammography were 0.87 (95% CI: 0.81-0.94) and 0.77 (95% CI: 0.68-0.86), respectively. Detection of breast lesions using the multimodal approach combining SOS images and mammograms, yielded an AUC of 0.89 (95% CI: 0.83-0.95), with statistically significant differences with respect to the use of mammograms alone (<i>p</i> = 0.0112).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our in silico experimental results demonstrate the feasibility of using USCT for breast lesion detection using fully automatic analysis of reconstructed SOS images. The multimodal approach, that combines radio-density and acoustic properties of the breast, outperforms the analysis using a single modality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2465-2474"},"PeriodicalIF":3.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525745","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 physics libraries for electronic brachytherapy Monte Carlo studies","authors":"Christian Valdes-Cortez,&nbsp;Iymad Mansour,&nbsp;David Santiago Ayala Alvarez,&nbsp;Francisco Berumen,&nbsp;Jean-Simon Côte,&nbsp;Gaël Ndoutoume-Paquet,&nbsp;Peter G. F. Watson,&nbsp;Jan Seuntjens,&nbsp;Facundo Ballester,&nbsp;Ernesto Mainegra-Hing,&nbsp;Rowan M. Thomson,&nbsp;Luc Beaulieu,&nbsp;Javier Vijande","doi":"10.1002/mp.17633","DOIUrl":"10.1002/mp.17633","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;Low-energy x-ray beams used in electronic brachytherapy (eBT) present significant dosimetric challenges due to their high depth-dose gradients, the dependence of detector response on materials, and the lack of standardized dose-to-water references. These challenges have driven the need for Monte Carlo (MC) simulations to ensure accurate dosimetry. However, discrepancies in the physics models used by different MC systems have raised concerns about their dosimetric consistency, particularly in modeling bremsstrahlung interactions.&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 assess the dosimetric impact of using different physics approaches in three state-of-the-art MC systems for eBT, focusing on the disagreements observed when different MC methods are used to evaluate bremsstrahlung interactions.&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 MC studies of the Axxent S700, the Esteya, and the INTRABEAM eBT systems were performed using two EGSnrc applications (egs_brachy and egs_kerma), TOPAS, and PENELOPE-2018 (PEN18). The fluence spectra and depth doses were compared for simplified x-ray tube models, which maintain the target mode (transmission or reflection), the target material and thickness, and the surface applicators’ source-to-surface distance. An extra simulation was made to evaluate the utility of the simplified models as proxies in predicting the most important characteristics of an accurate applicator's simulation (detailed model of INTRABEAM's 30 mm surface applicator). The EGSnrc applications and PEN18 utilized their default bremsstrahlung angular emission approaches. TOPAS used two physics lists: g4em-livermore (TOPAS&lt;sub&gt;liv&lt;/sub&gt;) and g4em-penelope (TOPAS&lt;sub&gt;pen&lt;/sub&gt;).&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 most significant differences between MC codes were observed for the transmission target mode. The bremsstrahlung component of the fluence spectra differed by about 15% on average, comparing PEN18, EGSnrc applications, and TOPAS&lt;sub&gt;liv&lt;/sub&gt;, with PEN18's fluences consistently lower. EGSnrc and PEN18 agreed within 3% for their characteristic spectrum components. However, PEN18's characteristic lines overreached TOPAS&lt;sub&gt;liv&lt;/sub&gt;’s by 40%. Those spectral characteristics generated depth dose differences, where PEN18, on average, scored 9% lower than EGSnrc and TOPAS&lt;sub&gt;liv&lt;/sub&gt;. Considering TOPAS&lt;sub&gt;pen&lt;/sub&gt; in the transmission mode, PEN18's fluence spectrum presented a lower bremsstrahlung (5%) but a higher characteristic component (10%); these spectral differences ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2520-2532"},"PeriodicalIF":3.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506687","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":"Towards an adequate description of the dose-response relationship in BNCT of glioblastoma multiforme","authors":"Barbara Marcaccio,&nbsp;Marco Crepaldi,&nbsp;Ian Postuma,&nbsp;Erica Simeone,&nbsp;Claretta Guidi,&nbsp;Setareh Fatemi,&nbsp;Ricardo Luis Ramos,&nbsp;Valerio Vercesi,&nbsp;Cinzia Ferrari,&nbsp;Laura Cansolino,&nbsp;Elena Delgrosso,&nbsp;Riccardo Di Liberto,&nbsp;Daniele Dondi,&nbsp;Dhanalakshmi Vadivel,&nbsp;Yi-Wei Chen,&nbsp;Fong-In Chou,&nbsp;Jinn-Jer Peir,&nbsp;Chuan-Jen Wu,&nbsp;Hui-Yu Tsai,&nbsp;Jia-Cheng Lee,&nbsp;Agustina Mariana Portu,&nbsp;Ana Mailén Dattoli Viegas,&nbsp;Sara Josefina González,&nbsp;Silva Bortolussi","doi":"10.1002/mp.17693","DOIUrl":"10.1002/mp.17693","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;Boron Neutron Capture Therapy (BNCT) is a binary radiotherapy based on the intravenous administration of a borated drug to the patient and the subsequent irradiation with a low-energy neutron beam. The borated formulation accumulates in the tumor cells, and when neutrons interact with boron, a nuclear capture reaction occurs, releasing high-linear energy transfer, short-range particles that cause lethal damage to the cancer cells. Due to its selectivity, BNCT has the potential to treat aggressive brain tumors such as glioblastoma multiforme (GBM), minimizing the side effects. GBM is a brain neoplasia that poses significant treatment challenges due to its invasiveness and resistance to conventional treatments.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This work aims to find a suitable model for calculating the photon isoeffective dose for GBM, producing ad hoc radiobiological data to feed the model.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;div&gt;To describe adequately the dose-effect relation of BNCT for GBM, the following strategy has been applied \u0000\u0000 &lt;ul&gt;\u0000 \u0000 &lt;li&gt;&lt;span&gt;1. &lt;/span&gt;We studied the impact of choosing two different photon radiation types (x- or gamma- rays)&lt;/li&gt;\u0000 \u0000 &lt;li&gt;&lt;span&gt;2. &lt;/span&gt;We assumed that the correct description of the photon-equivalent dose is obtained with the photon isoeffective dose model. This model calculates the photon dose that equals the cell survival obtained with BNCT, taking into account synergism and sub-lethal damage (SLD).&lt;/li&gt;\u0000 \u0000 &lt;li&gt;&lt;span&gt;3. &lt;/span&gt;Survival curves as a function of the dose for the human GBM U87 cell line were constructed using the clonogenic assays for irradiation with photons (reference), neutron beam, and BNCT.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;&lt;span&gt;4. &lt;/span&gt;Survival curves were fitted with the modified linear quadratic model, using SLD repair times derived for U87. The radiobiological parameters were determined for the photon isoeffective dose model.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;&lt;span&gt;5. &lt;/span&gt;The model was applied to a clinical case that received BNCT in Taiwan. Treatment planning has been simulated using an accelerator-based designed neutron beam following the real treatment process and parameters. The results were discussed and compared to the current method, which employs relative biological effectiveness (RBE) factors to obtain BNCT dosimetry in photon-equivalent units.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;/div&gt;\u0000 &lt;/section&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2606-2617"},"PeriodicalIF":3.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477311","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":"Label-efficient sequential model-based weakly supervised intracranial hemorrhage segmentation in low-data non-contrast CT imaging","authors":"Shreyas H Ramananda,&nbsp;Vaanathi Sundaresan","doi":"10.1002/mp.17689","DOIUrl":"10.1002/mp.17689","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;In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) in emergency stroke imaging for severity assessment. However, compared to magnetic resonance imaging (MRI), ICH shows low contrast and poor signal-to-noise ratio on NCCT images. Accurate automated segmentation of ICH lesions using deep learning methods typically requires a large number of voxelwise annotated data with sufficient diversity to capture ICH characteristics.&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 reduce the requirement for voxelwise labeled data, in this study, we propose a weakly supervised (WS) method to segment ICH in NCCT images using image-level labels (presence/absence of ICH). Obtaining such image-level annotations is typically less time-consuming for clinicians. Hence, determining ICH segmentation from image-level labels provides highly time- and manually resource-efficient site-specific solutions in clinical emergency point-of-care (POC) settings. Moreover, because clinical datasets often consist of a limited amount of data, we show the utility of image-level annotated large datasets for training our proposed WS method to obtain a robust ICH segmentation in large as well as low-data regimes.&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;Our proposed WS method determines the location of ICH using class activation maps (CAMs) from image-level labels and further refines ICH pseudo-masks in an unsupervised manner to train a segmentation model. Unlike existing WS methods for ICH segmentation, we used interslice dependencies across contiguous slices in NCCT volumes to obtain robust activation maps from the classification step. Additionally, we showed the effect of a large dataset on low-data regimes by comparing the WS segmentation trained on a large dataset with the baseline performance in low-data regimes. We used the radiological society of North America (RSNA) dataset (21,784 subjects) as a large dataset and the INSTANCE (100 subjects) and PhysioNet (75 subjects) datasets as low-data regimes. In addition, we performed the first ever investigation of the minimum amount (lower bound) of training data (from a large dataset) required for robust ICH segmentation performance in low-data regimes. We also evaluated the performance of our model across different ICH subtypes. In RSNA, 541 2D slices were designated for annotation and held as test data. The remaining samples were divided, with training:testing of 90%:10%. For INSTANCE and PhysioNet, the data were divided into five-fold for cross validation.&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;Using only 50% of ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2123-2144"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443127","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":"Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy","authors":"John C. Asbach,&nbsp;Anurag K. Singh,&nbsp;Austin J. Iovoli,&nbsp;Mark Farrugia,&nbsp;Anh H. Le","doi":"10.1002/mp.17672","DOIUrl":"10.1002/mp.17672","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;Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to treatment will vary based on their specific anatomy and the proposed treatment plan—these factors are spatial and closely related. However, additional factors may also have importance, such as non-spatial descriptive clinical characteristics, which can be structured as tabular data. It is critical to provide models with as comprehensive of a patient representation as possible, but inputs with differing data structures are incompatible in raw form; traditional models that consider these inputs require feature engineering prior to modeling. In neural networks, feature engineering can be organically integrated into the model itself, under one governing optimization, rather than performed prescriptively beforehand. However, the native incompatibility of different data structures must be addressed. Methods to reconcile structural incompatibilities in multimodal model inputs are called data fusion. We present a novel joint early pre-spatial (JEPS) fusion technique and demonstrate that differences in fusion approach can produce significant model performance differences even when the data is identical.&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 present a novel pre-spatial fusion technique for volumetric neural networks and demonstrate its impact on model performance for pretreatment prediction of overall survival (OS).&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;From a retrospective cohort of 531 head and neck patients treated at our clinic, we prepared an OS dataset of 222 data-complete cases at a 2-year post-treatment time threshold. Each patient's data included CT imaging, dose array, approved structure set, and a tabular summary of the patient's demographics and survey data. To establish single-modality baselines, we fit both a Cox Proportional Hazards model (CPH) and a dense neural network on only the tabular data, then we trained a 3D convolutional neural network (CNN) on only the volume data. Then, we trained five competing architectures for fusion of both modalities: two early fusion models, a late fusion model, a traditional joint fusion model, and the novel JEPS, where clinical data is merged into training upstream of most convolution operations. We used standardized 10-fold cross validation to directly compare the performance of all models on identical train/test splits of patients, using area under the receiver-operator curve (AUC) as the primary performance metric.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2675-2687"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384570","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":"Chinese reference population: Open-source age-dependent computational phantoms of reference Chinese population","authors":"Siyi Huang,&nbsp;Qian Liu,&nbsp;Tianwu Xie","doi":"10.1002/mp.17670","DOIUrl":"10.1002/mp.17670","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;Computational phantoms have been widely used in radiation protection, radiotherapy, medical imaging, surgery navigation, and digital anatomy. However, current Chinese phantoms lack representation for all sensitive groups including adults, children, and pregnant women. This manuscript aims to address this gap by developing novel open-access computational phantoms representing the Chinese population.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Acquisition and validation methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The Chinese reference population (CRP) developed in this study includes 30 phantoms, available in both voxel and nonuniform rational B-spline (NURBS) formats, with ages in 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 18 years, and adult male and female, as well as four pregnant women in early pregnancy, first trimester, second trimester, and third trimester. The development process involved image segmentation, NURBS reconstruction, and voxelization based on whole-body computed tomography (CT) scans of 22 original individual patients. Reference organ masses were directly obtained from the Chinese Reference Human Anatomical Physiological and Metabolic Data, as well as international commission on radiological protection (ICRP) Publication 89.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data format and usage notes&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Voxelized phantoms are accessible in DAT format as raw data, which can be opened by medical imaging softwares such as a medical image data analysis tool (AMIDE). Excel files contain descriptive information (ages, genders, phantom sizes, voxel sizes, organ masses, densities) and organ absorbed doses on &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mn&gt;18&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mi&gt;G&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$^{18}F-FDG$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; application. All data in this study can be obtained from our official website (https://alldigitaltwins.com) and Zenodo (https://zenodo.org/records/14268606).&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;This work offers a collection of open-source age-dependent phantoms featuring anatomical data specific to the Chinese population. Researchers can utilize this dataset to modify and adapt the phantoms for specific applications, fostering innovation and progress, and enhancing accuracy and applicability in various f","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2688-2696"},"PeriodicalIF":3.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367165","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}