Physical and Engineering Sciences in Medicine最新文献

Impact of automatic exposure control on radiation dose and detectability in dual-source and fast kV switching dual-energy CT. 自动照射控制对双源快速开关双能CT辐射剂量和可探测性的影响。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-05-06 DOI: 10.1007/s13246-026-01735-1

Kosuke Matsubara, Yoshinori Ogawa, Ryo Yoshikawa, Ayaka Hirosawa, Shoma Chiba, Chatnapa Nuntue, Khajonsak Tantiwetchayanon

{"title":"Impact of automatic exposure control on radiation dose and detectability in dual-source and fast kV switching dual-energy CT.","authors":"Kosuke Matsubara, Yoshinori Ogawa, Ryo Yoshikawa, Ayaka Hirosawa, Shoma Chiba, Chatnapa Nuntue, Khajonsak Tantiwetchayanon","doi":"10.1007/s13246-026-01735-1","DOIUrl":"https://doi.org/10.1007/s13246-026-01735-1","url":null,"abstract":"We evaluated radiation dose and detectability changes with automatic exposure control (AEC) according to object size in dual-source (DS) and fast kV switching (FS) dual-energy computed tomography (DECT). A phantom with five section diameters (16-36 cm) was scanned using different AEC settings (DS: Quality Reference mAs [QRmAs] 300-700; FS: Noise Index [NI] 8-12). Volume CT dose index (CTDIvol) and detectability index (d') for iodine were measured. Clinical CTDIvol data from 40 to 80 kg patients undergoing liver dynamic DECT were retrospectively analyzed. In DS-DECT, CTDIvol increased slightly with section diameter but plateaued at QRmAs 600-700 for 31-36 cm (31 cm: 24.1 mGy; 36 cm: 22.5-22.7 mGy), and d' decreased for larger sections. Clinical CTDIvol did not differ significantly among weight groups (40-<50 kg: 21.5 mGy; 50-<60 kg: 22.2 mGy; 60-<70 kg: 22.8 mGy; mean; p = 0.13). In FS-DECT, CTDIvol and d' varied with NI and section diameter: for the 26-cm section, CTDIvol ranged from 15.0 to 30.8 mGy and d' from 37.5 to 59.0; for 36-cm section, CTDIvol was 39.9 mGy and d' 24.0-27.8, with smaller variations than single-energy CT (SECT). Clinical CTDIvol increased with patient weight up to 70 kg (40-<50 kg: 20.3 mGy, 50-<60 kg: 25.8 mGy, 60-<70 kg: 29.2 mGy; mean; p < 0.05). AEC behavior in DECT differs from SECT, causing variations in dose and detectability. Appropriate AEC settings in DECT can achieve image quality comparable to SECT.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monte Carlo-based dosimetric comparison of CCA and CCB radioactive eye plaques for uveal melanoma. 基于蒙特卡罗的CCA和CCB放射性眼斑治疗葡萄膜黑色素瘤的剂量学比较。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-05-06 DOI: 10.1007/s13246-026-01701-x

Parvin Ahmadi, Reyhaneh Gili

{"title":"Monte Carlo-based dosimetric comparison of CCA and CCB radioactive eye plaques for uveal melanoma.","authors":"Parvin Ahmadi, Reyhaneh Gili","doi":"10.1007/s13246-026-01701-x","DOIUrl":"https://doi.org/10.1007/s13246-026-01701-x","url":null,"abstract":"This study conducted a comprehensive dosimetric analysis of three beta-emitting radionuclides ⁹⁰Sr, ¹⁰⁶Ru, and ¹⁴²Pr embedded in two ophthalmic plaque models, the Concave Circular Applicator (CCA) and Concave Circular Brachytherapy (CCB), with different backing materials (gold, silver, and gadolinium). Using Geant4-based Monte Carlo simulations, dose distributions within ocular structures were analyzed to optimize tumor coverage while minimizing radiation exposure to critical tissues. Validation with EBT3 radiochromic films demonstrated dose discrepancies below 5%, confirming the accuracy of the model. The results showed that ¹⁴²Pr combined with a gadolinium-backed CCB plaque achieved the most favorable therapeutic ratio, delivering a high tumor dose while reducing radiation to the lens and optic nerve. Gadolinium exhibited an advantageous balance between backscatter enhancement and moderate bremsstrahlung emission compared to gold and silver. These findings highlight the potential of ¹⁴²Pr-Gd-CCB configurations for personalized ocular brachytherapy, particularly for small to medium uveal melanomas. Future research should integrate patient-specific anatomical models to further optimize treatment planning and clinical translation.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of overlap volumes-based IMRT Rapidplan for cervical cancer patients with the aim of OARs sparing. 以重叠体积为基础的宫颈癌患者IMRT快速计划的探索，目的是保留OARs。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-29 DOI: 10.1007/s13246-026-01732-4

Minfeng Huang, Changfei Gong, Biaoshui Liu, Yili Wang, Jinghua Zhong, Min Wang, Jun Chi, Li Xinyuan, Ruilian Xie, Chunbo Tang

引用次数: 0

Comparison of surface-guided radiotherapy-based deep inspiration breath hold and continuous positive airway pressure techniques in left-sided breast cancer: dosimetric and workflow analysis. 基于表面引导放射治疗的深度吸气屏气与持续气道正压技术治疗左侧乳腺癌的比较：剂量学和工作流程分析。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-29 DOI: 10.1007/s13246-026-01733-3

Su Chul Han, Jason Joon Bock Lee, Jin Dong Cho, Il Sun Jeong, Heerim Nam, Hyebin Lee

引用次数: 0

An IoT-enabled heart disease prediction framework using hybrid and multi-dilated convolution aided adaptive residual attention network. 一个基于物联网的心脏病预测框架，使用混合和多重扩张卷积辅助自适应剩余注意网络。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-21 DOI: 10.1007/s13246-026-01727-1

A Moorthy, V Sarala, C Sivasankar, S Parthiban, U Samson Ebenezar

{"title":"An IoT-enabled heart disease prediction framework using hybrid and multi-dilated convolution aided adaptive residual attention network.","authors":"A Moorthy, V Sarala, C Sivasankar, S Parthiban, U Samson Ebenezar","doi":"10.1007/s13246-026-01727-1","DOIUrl":"https://doi.org/10.1007/s13246-026-01727-1","url":null,"abstract":"In the digital era, the medical industry creates a large amount of information related to patients. Manual processing of this produced information by a physician is very difficult. Therefore, the Internet of Things (IoT)-enabled heart disease detection is currently gaining high attention from various technical fields, particularly for personalized medical care. Still, in several cases, efficient detection of heart disease and 24-h consultation with an expert is not possible because of various reasons. Additionally, there are a lot of heart-related deaths, and the death count is rising every day. Prediction and detection of heart disease need high perfection and precision since a minor error could result in a serious condition or the death of an individual. So, an IoT-based network is developed in this paper to tackle these issues. The introduced approach is implemented in two phases. At the beginning phase, the required signal is collected and it is converted into spectrogram images with the help of a Short-Time Fourier Transform. In the second phase, sensor data are collected using IoT devices. This collected sensor data and the spectrogram images are given to the Hybrid and Multi-dilated Convolution based Adaptive Residual Attention Network (HMDCARAN) for predicting heart disease. The suggested HMDCARAN's parameters are tuned by the Modified Crayfish Optimization Algorithm. The outcome of the implemented network is compared with the traditional approaches to verify its effectiveness. Here, the developed framework achieved an accuracy of 96.52%, precision of 98.29%, and sensitivity of 97.18, which is enhanced than the other frameworks. Thus, the outcome proved that the designed network can identify heart disease in the initial stage and overcome the risk factors caused at the advanced stages of the heart disorder.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dosiomics versus DVH-based machine learning models for predicting shoulder impairment in breast cancer patients. 剂量组学与基于dvh的机器学习模型预测乳腺癌患者肩部损伤。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-08 DOI: 10.1007/s13246-026-01718-2

Moghadaseh Khalghi Bizaki, Mohammad Fasih, Zahra Bgherpour, Pedram Fadavi, Majid Akhavan Hejazi Seyed, Donya Davoodi, Sepideh Soltani, Mojtaba Safari, Manijeh Beigi

引用次数: 0

MRI-based brain tumor detection through an explainable EfficientNetV2 and MLP-mixer attention architecture. 通过可解释的EfficientNetV2和MLP-mixer注意力架构进行基于mri的脑肿瘤检测。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-07 DOI: 10.1007/s13246-026-01728-0

Mustafa Yurdakul, Şakir Taşdemir

引用次数: 0

CNNT-Net: a novel deep learning-based framework for estimating high-quality fiber orientation distributions from single-shell dMRI. CNNT-Net：一种新的基于深度学习的框架，用于从单壳dMRI中估计高质量的纤维方向分布。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-02 DOI: 10.1007/s13246-026-01723-5

Yan Fan, Jiahao Li, Yingying Yao, Lingmei Ai

{"title":"CNNT-Net: a novel deep learning-based framework for estimating high-quality fiber orientation distributions from single-shell dMRI.","authors":"Yan Fan, Jiahao Li, Yingying Yao, Lingmei Ai","doi":"10.1007/s13246-026-01723-5","DOIUrl":"https://doi.org/10.1007/s13246-026-01723-5","url":null,"abstract":"This study aims to introduce a novel deep learning-based network for the accurate reconstruction of high-quality fiber orientation distribution (FOD) images in the context of non-invasive imaging of white matter tractogram. The proposed network utilizes the angular correlation coefficient (ACC) as a threshold and employs a voxel selection module to categorize the voxels into two sizes. It integrates convolutional neural networks (CNN) and Transformers for estimating the FOD. Additionally, in instances where complex fiber configurations result in insufficient spherical harmonics (SH) coefficients estimated by the CNN below the threshold, the Transformer with two innovative attention mechanisms replaces the CNN's coefficients to enhance FOD estimation accuracy. Experiments conducted on the Human Connectome Project (HCP) dataset demonstrate promising qualitative and quantitative outcomes, showcasing the robustness of the proposed method in producing high-quality FOD images. The developed deep learning-based network presents a viable solution for enhancing the clinical applicability of high-quality FOD imaging, offering a reliable reference for non-invasive imaging of white matter tractogram.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of radiation permeability of CoCrMo and Ti-6Al-4V implants by Monte Carlo simulation of mass attenuation coefficients. 利用质量衰减系数蒙特卡罗模拟评估CoCrMo和Ti-6Al-4V植入物的辐射渗透率。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-02 DOI: 10.1007/s13246-026-01729-z

Kamil Savaş, Cüneyt May, Atakan Küskün, Yiğit Ali Üncü

{"title":"Assessment of radiation permeability of CoCrMo and Ti-6Al-4V implants by Monte Carlo simulation of mass attenuation coefficients.","authors":"Kamil Savaş, Cüneyt May, Atakan Küskün, Yiğit Ali Üncü","doi":"10.1007/s13246-026-01729-z","DOIUrl":"https://doi.org/10.1007/s13246-026-01729-z","url":null,"abstract":"Metallic implants remain a major source of artifact formation in computed tomography (CT) imaging, yet the underlying material dependent attenuation mechanisms are not fully characterized. This study provides a comprehensive quantitative comparison of the photon-attenuation properties of cobalt-chromium-molybdenum (CoCrMo) and titanium-aluminum-vanadium (Ti-6Al-4V) orthopedic implant alloys using Monte Carlo N-Particle (MCNP6) simulations across the diagnostic energy range of 50-150 keV, supported by theoretical data from the XCOM photon cross-section database. Radiation attenuation analysis showed that CoCrMo alloy provides higher shielding efficiency than Ti-6Al-4V alloy, which is consistent with the more pronounced computed tomography artifacts observed in patients with CoCrMo hemiarthroplasty implants. The excellent agreement between MCNP simulations and XCOM data (deviation < 1%) validates the robustness of the simulation framework. These results demonstrate that alloy composition and density are primary determinants of photon material interactions and directly influence metal artifact severity, providing a physics-based rationale for material specific CT protocol optimization, implant selection considerations, and advanced artifact reduction strategies.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images. 多模态注意力增强网络从灌注图像中分割急性缺血性脑卒中。

IF 2 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-04-01 DOI: 10.1007/s13246-026-01730-6

Xuanhe Zhao, Shannan Chen, Patrice Monkam, Ronghui Ju, Peizhuo Zang, Chao Li, Shouliang Qi

{"title":"Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.","authors":"Xuanhe Zhao, Shannan Chen, Patrice Monkam, Ronghui Ju, Peizhuo Zang, Chao Li, Shouliang Qi","doi":"10.1007/s13246-026-01730-6","DOIUrl":"https://doi.org/10.1007/s13246-026-01730-6","url":null,"abstract":"Acute ischemic stroke (AIS) is a major cause of long-term disability and mortality worldwide. Accurate segmentation of stroke lesions, particularly the infarct core and penumbra, is critical for effective treatment planning. In this study, we propose a deep learning-based multimodal segmentation network to improve the accuracy of AIS lesion delineation. The proposed framework consists of independent encoders, Multimodal Spatial and Channel Fusion (MSCF) modules, and Decoder Spatial and Channel Attention (DSCA) modules. Independent encoders are used to extract modality-specific features, while the MSCF module integrates complementary information across modalities. The DSCA module is introduced in the decoder to refine feature fusion between encoder representations and decoder features. The model was trained and evaluated on the ISLES SPES 2015, ISLES 2017, and ISLES 2018 datasets using Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95), Recall, and Precision. On the ISLES SPES 2015 dataset, the proposed method achieved a DSC of 83.91%, HD95 of 4.16 mm, Recall of 78.23%, and Precision of 88.63%. On the ISLES 2017 and ISLES 2018 datasets, the model achieved DSC values of 41.32% and 54.90%, with HD95 values of 25.10 mm and 23.06 mm, respectively. These results demonstrate that the proposed multimodal fusion strategy effectively improves segmentation accuracy and robustness for AIS lesion segmentation.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0