Physical and Engineering Sciences in Medicine最新文献

{"title":"Ecg-based arrhythmia classification using discrete wavelet transform and attention-enhanced CNN-BiGRU model.","authors":"Xiaosong He, Chuanli Hu, Kai Ma, Jie Huang, Hongjing He","doi":"10.1007/s13246-025-01639-6","DOIUrl":"https://doi.org/10.1007/s13246-025-01639-6","url":null,"abstract":"<p><p>Electrocardiogram (ECG)-based arrhythmia classification is crucial for the early detection and diagnosis of cardiovascular diseases. However, the presence of noise in raw ECG signals presents significant challenges to classification performance. In this study, we propose a novel approach that combines discrete wavelet transform (DWT) for signal denoising with an Attention-Enhanced Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for arrhythmia classification. First, DWT is applied to eliminate noise while preserving essential morphological features of the ECG signals. To address class imbalance, the Borderline-SMOTE algorithm is applied to generate synthetic samples for minority classes. The preprocessed signals are then passed through a CNN for hierarchical feature extraction, followed by a BiGRU to capture temporal dependencies. An attention mechanism is integrated to emphasize the most informative regions of the signal, enhancing the model's discriminative capability. The proposed method was evaluated on the MIT-BIH arrhythmia database and achieved an accuracy of 99.22% across five arrhythmia categories, outperforming several existing methods. This approach provides an effective solution for automatic arrhythmia detection in clinical practice.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070724","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}

{"title":"A full-scale attention-augmented CNN-transformer model for segmentation of oropharyngeal mucosa organs-at-risk in radiotherapy.","authors":"Lian He, Jianda Sun, Shanfu Lu, Jingyang Li, Xiaoqing Wang, Ziye Yan, Jian Guan","doi":"10.1007/s13246-025-01614-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01614-1","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Radiation-induced oropharyngeal mucositis (ROM) is a common and severe side effect of radiotherapy in nasopharyngeal cancer patients, leading to significant clinical complications such as malnutrition, infections, and treatment interruptions. Accurate delineation of the oropharyngeal mucosa (OPM) as an organ-at-risk (OAR) is crucial to minimizing radiation exposure and preventing ROM. This study aims to develop and validate an advanced automatic segmentation model, attention-augmented Swin U-Net transformer (AA-Swin UNETR), for accurate delineation of OPM to improve radiotherapy planning and reduce the incidence of ROM. We proposed a hybrid CNN-transformer model, AA-Swin UNETR, based on the Swin UNETR framework, which integrates hierarchical feature extraction with full-scale attention mechanisms. The model includes a Swin Transformer-based encoder and a CNN-based decoder with residual blocks, connected via a full-scale feature connection scheme. The full-scale attention mechanism enables the model to capture long-range dependencies and multi-level features effectively, enhancing the segmentation accuracy. The model was trained on a dataset of 202 CT scans from Nanfang Hospital, using expert manual delineations as the gold standard. We evaluated the performance of AA-Swin UNETR against state-of-the-art (SOTA) segmentation models, including Swin UNETR, nnUNet, and 3D UX-Net, using geometric and dosimetric evaluation parameters. The geometric metrics include Dice similarity coefficient (DSC), surface DSC (sDSC), volume similarity (VS), Hausdorff distance (HD), precision, and recall. The dosimetric metrics include changes of D&lt;sub&gt;0.1 cc&lt;/sub&gt; and D&lt;sub&gt;mean&lt;/sub&gt; between results derived from manually delineated OPM and auto-segmentation models. The AA-Swin UNETR model achieved the highest mean DSC of 87.72 ± 1.98%, significantly outperforming Swin UNETR (83.53 ± 2.59%), nnUNet (85.48%± 2.68), and 3D UX-Net (80.04 ± 3.76%). The model also showed superior mean sDSC (98.44 ± 1.08%), mean VS (97.86 ± 1.43%), mean precision (87.60 ± 3.06%) and mean recall (89.22 ± 2.70%), with a competitive mean HD of 9.03 ± 2.79 mm. For dosimetric evaluation, the proposed model generates smallest mean [Formula: see text] (0.46 ± 4.92 cGy) and mean [Formula: see text] (6.26 ± 24.90 cGY) relative to manual delineation compared with other auto-segmentation results (mean [Formula: see text] of Swin UNETR = -0.56 ± 7.28 cGy, nnUNet = 0.99 ± 4.73 cGy, 3D UX-Net = -0.65 ± 8.05 cGy; mean [Formula: see text] of Swin UNETR = 7.46 ± 43.37, nnUNet = 21.76 ± 37.86 and 3D UX-Net = 44.61 ± 62.33). In this paper, we proposed a transformer and CNN hybrid deep-learning based model AA-Swin UNETR for automatic segmentation of OPM as an OAR structure in radiotherapy planning. Evaluations with geometric and dosimetric parameters demonstrated AA-Swin UNETR can generate delineations close to a manual reference, both in terms of geometry and dose-volume metrics. The proposed model out-pe","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034335","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}

{"title":"A comprehensive investigation of the radiation isocentre spatial variability in linear accelerators: implications for commissioning, QA, and clinical protocols.","authors":"Zhen Hui Chen, Hans Lynggaard Riis, Rohen White, Thomas Milan, Pejman Rowshanfarzad","doi":"10.1007/s13246-025-01637-8","DOIUrl":"https://doi.org/10.1007/s13246-025-01637-8","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030942","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}

{"title":"Prop scan versus roll scan: selection for cranial three-dimensional rotational angiography using in-house phantom and Figure of Merit as parameter.","authors":"Ika Hariyati, Ani Sulistyani, Matthew Gregorius, Harimulti Aribowo, Ungguh Prawoto, Defri Dwi Yana, Thariqah Salamah, Lukmanda Evan Lubis, Djarwani Soeharso Soejoko","doi":"10.1007/s13246-025-01632-z","DOIUrl":"https://doi.org/10.1007/s13246-025-01632-z","url":null,"abstract":"<p><p>This study introduces a novel optimization framework for cranial three-dimensional rotational angiography (3DRA), combining the development of a brain equivalent in-house phantom with Figure of Merit (FOM) a quantitative evaluation method. The technical contribution involves the development of an in-house phantom constructed using iodine-infused epoxy and lycal resins, validated against clinical Hounsfield Units (HU). A customized head phantom was developed to simulate brain tissue and cranial vasculature for 3DRA optimization. The phantom was constructed using epoxy resin with 0.15-0.2% iodine to replicate brain tissue and lycal resin with iodine concentrations ranging from 0.65 to 0.7% to simulate blood vessels of varying diameters. The phantom materials validation was performed by comparing their HU values to clinical reference HU values from brain tissue and cranial vessels, ensuring accurate tissue simulation. The validated phantom was used to acquire images using cranial 3DRA protocols, specifically Prop-Scan and Roll-Scan. Image quality was assessed using Signal-Difference-to-Noise Ratio (SDNR), Dose-Area Product (DAP), and Modulation Transfer Function (MTF). Imaging efficiency was quantified using the Figure of Merit (FOM), calculated as SDNR²/DAP, to objectively compare the performance of two cranial 3DRA protocols. The task-based optimization showed that Roll-Scan consistently outperformed Prop-Scan across all vessel sizes and regions. Roll-Scan yields FOM values ranging from 183 to 337, while Prop-Scan FOM values ranged from 96 to 189. Additionally, Roll-Scan (0.27 lp/pixel) delivered better spatial resolution, as indicated by higher MTF 10% value than Prop-Scan (0.23 lp/pixel). Most notably, Roll-Scan consistently detecting 2 mm vessel structures among all regions of the phantom. This capability is clinically important in cerebral angiography, which is accurate visualization of small vessels, i.e. the Anterior Cerebral Artery (ACA), Posterior Cerebral Artery (PCA), and Middle Cerebral Artery (MCA). These findings highlight Roll-Scan as the superior protocol for brain interventional imaging, underscoring the significance of FOM as a comprehensive parameter for optimizing imaging protocols in clinical practice. The experimental results support the use of the Roll-Scan protocol as the preferred acquisition method for cerebral angiography in clinical practice. The analysis using FOM provides substantial and quantifiable evidence in determining the acquisition methods. Furthermore, the customized in-house phantom is recommended as a candidate to optimization tools for clinical medical physicists.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030952","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}

{"title":"Clinical evaluation of motion robust reconstruction using deep learning in lung CT.","authors":"Shiho Kuwajima, Daisuke Oura","doi":"10.1007/s13246-025-01633-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01633-y","url":null,"abstract":"<p><p>In lung CT imaging, motion artifacts caused by cardiac motion and respiration are common. Recently, CLEAR Motion, a deep learning-based reconstruction method that applies motion correction technology, has been developed. This study aims to quantitatively evaluate the clinical usefulness of CLEAR Motion. A total of 129 lung CT was analyzed, and heart rate, height, weight, and BMI of all patients were obtained from medical records. Images with and without CLEAR Motion were reconstructed, and quantitative evaluation was performed using variance of Laplacian (VL) and PSNR. The difference in VL (DVL) between the two reconstruction methods was used to evaluate which part of the lung field (upper, middle, or lower) CLEAR Motion is effective. To evaluate the effect of motion correction based on patient characteristics, the correlation between body mass index (BMI), heart rate and DVL was determined. Visual assessment of motion artifacts was performed using paired comparisons by 9 radiological technologists. With the exception of one case, VL was higher in CLEAR Motion. Almost all the cases (110 cases) showed large DVL in the lower part. BMI showed a positive correlation with DVL (r = 0.55, p < 0.05), while no differences in DVL were observed based on heart rate. The average PSNR was 35.8 ± 0.92 dB. Visual assessments indicated that CLEAR Motion was preferred in most cases, with an average preference score of 0.96 (p < 0.05). Using Clear Motion allows for obtaining images with fewer motion artifacts in lung CT.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030968","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}

{"title":"Achieving greater accuracy in transcranial magnetic stimulation corticospinal evaluation and motor mapping by improving motor evoked potential recording: an emerging issue.","authors":"Marco Antonio Cavalcanti Garcia, Ana Carolina Borges Valente, Victor Hugo Moraes, Daniela Morales, Lucas Dos Santos Betioli","doi":"10.1007/s13246-025-01638-7","DOIUrl":"https://doi.org/10.1007/s13246-025-01638-7","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030909","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}

{"title":"A review of image processing and analysis of computed tomography images using deep learning methods.","authors":"Darcie Anderson, Prabhakar Ramachandran, Jamie Trapp, Andrew Fielding","doi":"10.1007/s13246-025-01635-w","DOIUrl":"https://doi.org/10.1007/s13246-025-01635-w","url":null,"abstract":"<p><p>The use of machine learning has seen extraordinary growth since the development of deep learning techniques, notably the deep artificial neural network. Deep learning methodology excels in addressing complicated problems such as image classification, object detection, and natural language processing. A key feature of these networks is the capability to extract useful patterns from vast quantities of complex data, including images. As many branches of healthcare revolves around the generation, processing, and analysis of images, these techniques have become increasingly commonplace. This is especially true for radiotherapy, which relies on the use of anatomical and functional images from a range of imaging modalities, such as Computed Tomography (CT). The aim of this review is to provide an understanding of deep learning methodologies, including neural network types and structure, as well as linking these general concepts to medical CT image processing for radiotherapy. Specifically, it focusses on the stages of enhancement and analysis, incorporating image denoising, super-resolution, generation, registration, and segmentation, supported by examples of recent literature.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993950","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}

{"title":"An Australasian survey on the use of ChatGPT and other large language models in medical physics.","authors":"Stanley A Norris, Tomas Kron, Maeve Masterson, Mohamed K Badawy","doi":"10.1007/s13246-025-01571-9","DOIUrl":"10.1007/s13246-025-01571-9","url":null,"abstract":"<p><p>This study surveyed medical physicists in Australia and New Zealand on their use of large language models (LLMs), particularly ChatGPT. There is currently no literature on the application of ChatGPT and other LLMs by medical physicists. This survey targeted a mixed group of professionals, including clinical medical physicists, registrars, students, and other specialised roles. It reveals that many respondents integrate LLM platforms into their work for a broad range of tasks. Most participants reported efficiency gains, although fewer perceived improvements in the overall quality of their work. Despite these benefits, substantial concerns remain regarding data security, patient confidentiality, and the lack of established guidelines or professional training for using these tools in a clinical context. Further, the potential for sudden changes in accessibility and pricing, which could disproportionately impact developing countries and under-resourced departments, implies that other vulnerabilities may exist. These findings suggest the need for the medical physics community to come together and debate the careful balance between exploiting LLM platforms and developing clear best practices that implement robust risk management strategies.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1145-1153"},"PeriodicalIF":2.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112191","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}

{"title":"Automated multiclass segmentation of liver vessel structures in CT images using deep learning approaches: a liver surgery pre-planning tool.","authors":"Sahar Sarkar, Mahdiyeh Rahmani, Parastoo Farnia, Alireza Ahmadian, Nasser Mozayani","doi":"10.1007/s13246-025-01581-7","DOIUrl":"10.1007/s13246-025-01581-7","url":null,"abstract":"<p><p>Accurate liver vessel segmentation is essential for effective liver surgery pre-planning, and reducing surgical risks since it enables the precise localization and extensive assessment of complex vessel structures. Manual liver vessel segmentation is a time-intensive process reliant on operator expertise and skill. The complex, tree-like architecture of hepatic and portal veins, which are interwoven and anatomically variable, further complicates this challenge. This study addresses these challenges by proposing the UNETR (U-Net Transformers) architecture for the multi-class segmentation of portal and hepatic veins in liver CT images. UNETR leverages a transformer-based encoder to effectively capture long-range dependencies, overcoming the limitations of convolutional neural networks (CNNs) in handling complex anatomical structures. The proposed method was evaluated on contrast-enhanced CT images from the IRCAD as well as a locally dataset developed from a hospital. On the local dataset, the UNETR model achieved Dice coefficients of 49.71% for portal veins, 69.39% for hepatic veins, and 76.74% for overall vessel segmentation, while reaching Dice coefficients of 62.54% for vessel segmentation on the IRCAD dataset. These results highlight the method's effectiveness in identifying complex vessel structures across diverse datasets. These findings underscore the critical role of advanced architectures and precise annotations in improving segmentation accuracy. This work provides a foundation for future advancements in automated liver surgery pre-planning, with the potential to enhance clinical outcomes significantly. The implementation code is available on GitHub: https://github.com/saharsarkar/Multiclass-Vessel-Segmentation .</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1463-1472"},"PeriodicalIF":2.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627479","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}

{"title":"Functional division of the prefrontal lobe of the brain: coordinate-based neuroimaging meta-analysis.","authors":"Jiaofen Nan, Kaifan Zhang, Beike Chen, Panpan Xu, Gaodeng Fan, Siyuan Zhang, Xiaojuan Ba, Duan Li","doi":"10.1007/s13246-025-01555-9","DOIUrl":"10.1007/s13246-025-01555-9","url":null,"abstract":"<p><p>The prefrontal cortex (PFC) is known to play a crucial role in the complex process of human cognition such as emotion, memory, value and decision-making, and reward. However, the current functional subregion division is too coarse and the specific localization of clusters related to each task remains unclear. In the study, to clarify the specific role of different clusters in the PFC, a detailed subdivision of the prefrontal cortex was conducted based on evidence from human neuroimaging studies. Based on the predefined inclusion criteria, up to December 2022, a total of 186 studies related to emotion, memory, value decision-making, and reward were screened from the PubMed, Web of Science, and BrainMap functional databases. The Activation Likelihood Estimation (ALE) method was used to divide the prefrontal cortex regions by performing single-activation analysis, contrastive-activation analysis, and joint-activation analysis. The prefrontal brain area was divided into 15 functional sub-areas, each corresponding to unique activation or joint activation areas of emotion, memory, value and decision-making, and reward. The results showed that the prefrontal lobe of the brain plays different roles in the different subregions. The division of the prefrontal lobe has important clinical value for understanding and studying the relevant diseases.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"987-997"},"PeriodicalIF":2.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530454","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}