Biomedical Physics & Engineering Express最新文献

{"title":"Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network.","authors":"Manami Umezu, Yohan Kondo, Shota Ichikawa, Yuki Sasaki, Koji Kaneko, Toshiro Ozaki, Naoya Koizumi, Hiroshi Seki","doi":"10.1088/2057-1976/adeab5","DOIUrl":"10.1088/2057-1976/adeab5","url":null,"abstract":"<p><p>Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration of additional treatment after surgery. In this study, we developed a deep convolutional neural network (DCNN) model to predict recurrence within six years after surgery using preoperative contrast-enhanced computed tomography (CECT) images, which are widely available and effective for detecting distant metastases. This retrospective study included preoperative CECT images from 133 patients with invasive ductal carcinoma. The images were classified into recurrence and no-recurrence groups using ResNet-101 and DenseNet-201. Classification performance was evaluated using the area under the receiver operating curve (AUC) with leave-one-patient-out cross-validation. At the optimal threshold, the classification accuracies for ResNet-101 and DenseNet-201 were 0.73 and 0.72, respectively. The median (interquartile range) AUC of DenseNet-201 (0.70 [0.69-0.72]) was statistically higher than that of ResNet-101 (0.68 [0.66-0.68]) (p < 0.05). These results suggest the potential of preoperative CECT-based DCNN models to predict breast cancer recurrence without the need for additional invasive procedures.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of equivalent uniform RBE-weighted dose in hypofractionated proton therapy for ocular melanoma.","authors":"Pavitra Ramesh, Alexei V Trofimov, Ramesh Rengan, Alexei V Chvetsov","doi":"10.1088/2057-1976/adea7d","DOIUrl":"10.1088/2057-1976/adea7d","url":null,"abstract":"<p><p><i>Objective.</i>The equivalent uniform RBE-weighted dose (EUD_RBE) is computed in a model problem for hypofractionated proton therapy for ocular melanoma, considering the depth and dose dependence of the relative biological effectiveness (RBE).<i>Approach.</i>The EUD_RBEwas developed to compare the integrated cell survival in radiotherapy modalities with nonuniform distributions of the RBE and the physical dose. Our simulations of the EUD_RBEin hypofractionated proton radiotherapy are based on the linear quadratic (LQ) cell survival model from which the dose correction to the RBE can be evaluated using the theory of dual radiation action. This theory predicts that the higher LET radiation increases the linear component (<i>α</i>) of radiation damage, while the quadratic component (<i>β</i>) remains unchanged. The effect of depth dependence of the RBE was derived from a fit to experimental data across various spread-out Bragg peaks (SOBPs) and the distribution of the physical dose was considered uniform.<i>Main results</i>. There are two competing processes that affect the EUD_RBE: first, the EUD_RBEdecreases as the fractional dose increases, and second, the EUD_RBEincreases with increasing the relative fraction of tumors treated with high RBE at the distal edge of Bragg peak. Our simulations show that the combined effect of these two processes predicts an increase of the EUD_RBEby 9%-12% relative to the physical dose for the fractionation schedule 5 × 10 Gy (RBE) assuming a distal tumor margin of 5 mm.<i>Significance</i>. The increase in the RBE at the end of the proton range largely compensates for the decrease in the RBE for higher fractional proton doses, thus producing the EUD_RBEthat does not deviate substantially from the clinically used uniform value of RBE = 1.1. The EUD_RBEwill enable more optimized proton therapy plans and comparison with other modalities such as eye plaque brachytherapy.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silico CT simulations of deep learning generated heterogeneous phantoms.","authors":"C S Salinas, K Magudia, A Sangal, L Ren, W P Segars","doi":"10.1088/2057-1976/ade9c9","DOIUrl":"10.1088/2057-1976/ade9c9","url":null,"abstract":"<p><p>Current virtual imaging phantoms primarily emphasize geometric accuracy of anatomical structures. However, to enhance realism, it is also important to incorporate intra-organ detail. Because biological tissues are heterogeneous in composition, virtual phantoms should reflect this by including realistic intra-organ texture and material variation. We propose training two 3D Double U-Net conditional generative adversarial networks (3D DUC-GAN) to generate sixteen unique textures that encompass organs found within the torso. The model was trained on 378 CT image-segmentation pairs taken from a publicly available dataset with 18 additional pairs reserved for testing. Textured phantoms were generated and imaged using DukeSim, a virtual CT simulation platform. Results showed that the deep learning model was able to synthesize realistic heterogeneous phantoms from a set of homogeneous phantoms. These phantoms were compared with original CT scans and had a mean absolute difference of 46.15 ± 1.06 HU. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were 0.86 ± 0.004 and 28.62 ± 0.14, respectively. The maximum mean discrepancy between the generated and actual distribution was 0.0016. These metrics marked an improvement of 27%, 5.9%, 6.2%, and 28% respectively, compared to current homogeneous texture methods. The generated phantoms that underwent a virtual CT scan had a closer visual resemblance to the true CT scan compared to the previous method. The resulting heterogeneous phantoms offer a significant step toward more realistic in silico trials, enabling enhanced simulation of imaging procedures with greater fidelity to true anatomical variation.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting radial dose function of LDR brachytherapy sources in the calcified soft tissues through Monte Carlo simulation.","authors":"Najmeh Mohammadi, Keyhandokht Karimi-Shahri","doi":"10.1088/2057-1976/ade9ca","DOIUrl":"10.1088/2057-1976/ade9ca","url":null,"abstract":"<p><p>This study aimed to investigate the effect of tissue calcification on the dose distribution of low-energy brachytherapy seeds used in the treatment of prostate and breast tumors. To achieve this, simulations of the IR-¹²⁵I and¹³¹Cs-1 seeds were conducted using the MCNPX Monte Carlo (MC) code. Brachytherapy dosimetric parameters, such as the dose rate constant (Λ) and radial dose function (g (r)), were calculated in water following the TG-43U1 protocol and validated by comparing the results with data reported in other literature. The findings revealed significant uncertainty in the g(r) for calcified tissue of the prostate, adipose, and glandular breast compared to normal tissue. The g(r) value showed a notable increase in the vicinity of the seeds (within 1 cm from the center of the seeds), followed by a decrease. This trend was observed in all three tissues. The higher the percentage of calcification in the tissue, the more pronounced the changes. The g(r) increased by a factor of 266 and 102 for IR-¹²⁵I, and¹³¹Cs-1 seeds, respectively, in the 100% calcified prostate. The trend of g(r) changes aligned with the trend of changes in the mass attenuation coefficients calculated for the tissues at different calcification percentages. An exponential function (Aexp(-r/t)) was derived to predict g(r) across various calcification percentages. Additionally, the insights gained from this study may apply to other soft tissues.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised retinal image registration based on D-STUNet and progressive keypoint screening strategy.","authors":"Xiangyu Deng, Jiayi Kang","doi":"10.1088/2057-1976/ade9c6","DOIUrl":"10.1088/2057-1976/ade9c6","url":null,"abstract":"<p><p><i>Objective</i>. Retinal image registration improves the accuracy and validity of a doctor's diagnosis and holds a crucial role in the monitoring and treatment of associated diseases. However, most existing image registration methods have limitations in identifying retinal vascular features, making it difficult to achieve desirable results in retinal image registration tasks. To solve this problem, a fusion network of Swin Transformer and U-Net, improved by Differential Multi-scale Convolutional Block Attention Module with Residual Mechanism (DMCR), named D-STUNet, is proposed in conjunction with the designed Progressive Keypoint Screening (PKS) strategy.</p><p><strong>Approach: </strong>The D-STUNet network is primarily based on an encoder-decoder framework, and employs DMCR for the improvement and fusion of the Swin Transformer and U-Net networks. Among them, the DMCR module enhances the ability to focus on retinal vascular features, which effectively improves the accuracy of retinal image registration in the event of limited data. Simultaneously, the network introduces the PKS strategy to enable the gradual accumulation of effective keypoint information in the course of the training, which ensures that the keypoints are more concentrated in the retinal vascular region, thus enhancing the matching rate and overall detection effect.</p><p><strong>Main results: </strong>The registration validation is conducted on the publicly accessible dataset Fundus Image Registration Dataset (FIRE) and compare it with nine algorithms. The experimental results show that the algorithm achieves an acceptance rate of 98.50%, a failure rate of 0, and an inaccuracy rate of 1.50%. In the area under the curve (AUC) metric, AUC for the Easy group is 0.929, while the AUC for the Mod and Hard groups are 0.883 and 0.724, respectively. The mean area under the curve (mAUC) across all comparison algorithms is the highest, outperforming the second-best algorithm by 0.09. Although it did not reach the optimum in certain subcategories (such as AUC-easy), its overall performance is significantly superior to existing methods.</p><p><strong>Significance: </strong>The proposed network is able to effectively capture local features such as complex vascular structures in retinal images, providing a new method to improve the registration accuracy of retinal images.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subject-specific feature extraction approach for a three-class motor imagery-based brain-computer interface enabling navigation in a virtual environment: open-access framework.","authors":"Fardin Afdideh, Mohammad Bagher Shamsollahi","doi":"10.1088/2057-1976/aded19","DOIUrl":"https://doi.org/10.1088/2057-1976/aded19","url":null,"abstract":"<p><p>Brain-Computer Interface (BCI) is a system that aids individuals with disabilities to establish a novel communication channel between the brain and computer. Among various electrophysiological sources that can drive a BCI system, Motor Imagery (MI) facilitates more natural communication for users with motor disabilities, whereas electroencephalogram (EEG) is considered the most practical brain imaging modality. However, subject training is a critical aspect of such a type of BCI. One possible solution to address this challenge is to leverage the Virtual Reality (VR) technology. This study proposes a VR in MI- and EEG-based BCI (MI-EEG-BCI-VR) framework wherein users navigate a Virtual Environment (VE) following cue-based training, and employing a subject-specific feature extraction approach. The assigned task involves performing the left hand, right hand, and feet movement imagination to navigate from the start station to the end station as quickly as possible. The generated brain signals are collected using three bipolar EEG channels only. The proposed open-access MATLAB-based MI-EEG-BCI-VR framework was validated with eight healthy participants. One participant demonstrated satisfactory performance in navigating the VE. Notably, it achieved the highest performance of 82.28 5.11% for MI and 97.72 4.55% for Motor Execution (ME) after just a single training session.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation and validation of a fast Monte Carlo code as a secondary dose calculation engine for proton therapy with narrow beams.","authors":"Vadim P Moskvin, Austin Faught, Fakhriddin Pirlepesov, Fang Xie, Shahad Al-Ward, Marie Cohilis, Kevin Souris, Thomas E Merchant, Chia-Ho Hua","doi":"10.1088/2057-1976/ade9c8","DOIUrl":"10.1088/2057-1976/ade9c8","url":null,"abstract":"<p><p>This study presents the implementation and validation of the fast, simplified open-source MC code MCsquare as a secondary dose calculation engine for intensity-modulated proton therapy with narrow beams (the Gaussian-shaped beam spot with standard deviations as small as 1-2 mm) produced by a synchrotron-based system with minibeam modification. A proton therapy system was modeled with MCsquare, using commissioning data, and with TOPAS, an explicit multi-particle MC code. The computed dose distributions were compared with the patient-specific quality assurance (QA) measurements (203 measurements for 94 treatment fields), using gamma analysis with criteria of 3% and 3 mm. The dose distributions in the patient geometry defined by computed tomography (CT) images were simulated with MCsquare and TOPAS and compared. For the main beam, the gamma passing rates of the patient-specific QA averaged 99.4% and 97.9% for MCsquare and 99.2% and 98.5% for TOPAS, with and without range shifter use, respectively. For minibeams, the rate was 100% for both MC codes. The dose distributions calculated with TOPAS and MCsquare on the patient's CT were identical, within the statistical error of the simulation. The simulation time with MCsquare varied between 1 and 25 min per plan on a 16-core workstation with a 2% statistical error. The fast, simplified MCsquare and the slower TOPAS using explicit multi-particle transport produced statistically identical dose distributions. The results support using MCsquare as a secondary dose engine for narrow beams.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advancements in feature extraction and classification based bone cancer detection - a systematic review.","authors":"Kanimozhi S, Sivakumar Rajagopal, Ananthakrishna Chintanpalli","doi":"10.1088/2057-1976/ade8f8","DOIUrl":"10.1088/2057-1976/ade8f8","url":null,"abstract":"<p><p>Cancer is a deadly disease that occurs due to the uncontrolled growth of abnormal cells. Bone cancer is the third most occurring disease; approximately 10,000 patients suffer from bone cancer in India annually. It can lead to death if not diagnosed in the earlier stage. Bone cancer occurs in four stages as follows: in stage 1 cancer does not spread to other bone parts, in stage 2 cancer looks similar to stage 1 but becomes dangerous, in stage 3 cancer spreads to one or two bone parts and in stage 4 cancer spreads to other body parts. Timely diagnosis of bone cancer is challenging due to the unspecific indications that are similar to common musculoskeletal injuries, late visits of patients to the hospital and low intuition by the physician. The texture of diseased bone differs from that of healthy bone. Mostly in the dataset, the healthy and cancerous bone images have similar characteristics. Therefore, the development of automated systems is necessary to classify normal and abnormal scan images. The objective of this paper is to identify the studies on classification techniques in detecting bone cancer with five criteria: feature extraction methods, machine learning (ML) and deep learning (DL) techniques, advantages, disadvantages and classifier accuracy. The current study performed the systematic literature review of 129 studies selected based on the use of different feature extractions to extract the textural characteristics of the images that are fed into the ML and DL algorithms to classify the normal and subtypes of bone cancer images for better analysis. The review concludes that convolutional neural network classifier along with different textural feature extraction techniques like gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) detected the bone cancer with high accuracy compared to DL classification without feature extraction techniques in diagnosing the bone cancer. In this respect, this paper proposes a systematic review of types of bone cancer and recent advancements in feature extraction methods and classification involving deep learning and machine learning models to detect bone cancer with a higher accuracy rate.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation and validation of a channelized hotelling observer model (CHO) in an AIFM task group: a national multicentric study in x-ray angiography (XA), a comprehensive and wide-reaching approach.","authors":"Marco Bertolini, Valeria Trojani, Noemi Cucurachi, Laura Verzellesi, Elena Cantoni, Nico Lanconelli, Nicoletta Paruccini, Raffaele Villa, Chiara Ingraito, Mariagrazia Quattrocchi, Maria Antonietta Gilio, Valentina Ravaglia, Giovanna Venturi, Linhsia Noferini, Raffaella Soavi, Francesca Pietrobon, Ornella Ortenzia, Aldo Mazzilli, Diego Trevisan, Andrea Bruschi, Silvia Mazzocchi, Domenico Lizio, Felicita Luraschi, Andrea D'Alessio, Loredana D'Ercole, Monica Cavallari, Andrea Nitrosi, Mauro Iori, Caterina Ghetti","doi":"10.1088/2057-1976/addfdd","DOIUrl":"10.1088/2057-1976/addfdd","url":null,"abstract":"<p><p>This study aims to prove the feasibility of objectively characterizing clinical XA protocols by implementing a model observer. The model observer's performance aligns with a human observer's and is described using a simple and comprehensive figure of merit (FOM). The practical implications of this study, which utilized the Leeds TO10 phantom to acquire 146 imaging datasets in a fixed setup measuring kerma rate from four manufacturers and seven XA models by thirteen hospitals, are significant. The datasets were divided and analyzed into three main protocol categories (cardiac, neurological, and vascular) acquired with the field of view (FOV) locally used in that hospital. A 40-channel Gabor CHO was employed to analyze the datasets and calculate the contrast detail (CD) curves. A new figure of merit (FOM) tailored for the present task was calculated, accounting for image quality and kerma rate. The FOM demonstrated our observer model's ability to describe the XA protocol's optimization. Short-term reproducibility of selected XA protocols was within 10%. Smaller FOVs lowered long-term reproducibility in terms of FOM because the position of the dosimeter increasingly influenced the automatic exposure parameters. This study demonstrates the feasibility of using a CHO model observer to assess an angiography system's quality using a CD paradigm. The insights gained from this study will be instrumental in developing tolerance requirements for future quality assurance guides, enhancing the quality of X-ray angiography protocols, and improving patient care.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Branch Attention Fusion Network for Pneumonia Detection.","authors":"Tiezhu Li, Bingbing Li, Chao Zheng","doi":"10.1088/2057-1976/adebf5","DOIUrl":"https://doi.org/10.1088/2057-1976/adebf5","url":null,"abstract":"<p><p>Pneumonia, as a serious respiratory disease caused by bacterial, viral or fungal infections, is an important cause of increased morbidity and mortality in high-risk populations (e.g.the elderly, infants and young children, and immunodeficient patients) worldwide. Early diagnosis is decisive for improving patient prognosis. In this study, we propose a Dual-Branch Attention Fusion Network based on transfer learning, aiming to improve the accuracy of pneumonia classification in lung X-ray images. The model adopts a dual-branch feature extraction architecture: independent feature extraction paths are constructed based on pre-trained convolutional neural networks (CNNs) and structural spatial state models, respectively, and feature complementarity is achieved through a feature fusion strategy. In the fusion stage, a Self-Attention Mechanism is introduced to dynamically weight the feature representations of different paths, which effectively improves the characterisation of key lesion regions. The experiments are carried out based on the publicly available ChestX-ray dataset, and through data enhancement, migration learning optimisation and hyper-parameter tuning, the model achieves an accuracy of 97.78% on an independent test set, and the experimental results fully demonstrate the excellent performance of the model in the field of pneumonia diagnosis, which provides a new and powerful tool for the rapid and accurate diagnosis of pneumonia in clinical practice, and our methodology provides a high--performance computational framework for intelligent pneumonia Early screening provides a high-performance computing framework, and its architecture design of multipath and attention fusion can provide a methodological reference for other medical image analysis tasks.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}