Medical Engineering & Physics最新文献

{"title":"Functional brain response pattern under rubber hand illusion based on fNIRS","authors":"Pengju Liu ,&nbsp;Rong Liu ,&nbsp;Chih-Hsiu Cheng ,&nbsp;Lizhen Wang ,&nbsp;Yubo Fan","doi":"10.1016/j.medengphy.2025.104340","DOIUrl":"10.1016/j.medengphy.2025.104340","url":null,"abstract":"<div><div>The rubber hand illusion (RHI), where people experience a sense of ownership over a rubber hand, has been researched by various neuroimaging methods. Here we used functional near-infrared spectroscopy (fNIRS) to analyze the activation and functional connectivity of related brain regions under RHI. Meanwhile, three brain functional network parameters were calculated and analyzed: degree, clustering coefficient, and characteristic path length. fNIRS results showed that under RHI, the oxyhemoglobin (HbO) concentration increased in the prefrontal cortex (PFC), motor cortex (MC) and occipital lobe (OL). The functional connectivity between right PFC and bilateral OL was increased, while the connection level between left MC and bilateral OL was decreased. Brain network under RHI condition had smaller average degree, average clustering coefficient, and shorter average characteristic path length. Notably, the information processing and exchange functions of left MC seem to be weakened under RHI state, which was also partially corroborated by the reduced local efficiency shown in brain functional network analysis. Overall, we suggest that enhanced functional connectivity between the right MC, OL and PFC, and functional inhibition of the left MC were key to RHI production. The study significance lies in enhancing understanding of body ownership and sensory integration.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104340"},"PeriodicalIF":1.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845026","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":"Ensemble learning of deep CNN models and two stage level prediction of Cobb angle on surface topography in adolescents with idiopathic scoliosis","authors":"Mostafa Hassan ,&nbsp;Jose Maria Gonzalez Ruiz ,&nbsp;Nada Mohamed ,&nbsp;Thomaz Nogueira Burke ,&nbsp;Qipei Mei ,&nbsp;Lindsey Westover","doi":"10.1016/j.medengphy.2025.104332","DOIUrl":"10.1016/j.medengphy.2025.104332","url":null,"abstract":"<div><div>This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb Angle (CA) from Surface Topography (ST) scans in adolescents with Idiopathic Scoliosis (AIS). The aim is to minimize radiation exposure during critical growth periods by offering a reliable, non-invasive assessment tool. The efficacy of various CNN-based feature extractors—DenseNet121, EfficientNetB0, ResNet18, SqueezeNet, and a modified U-Net—was evaluated on a dataset of 654 ST scans using a regression analysis framework for accurate CA prediction. The dataset comprised 590 training and 64 testing scans. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and accuracy in classifying scoliosis severity (mild, moderate, severe) based on CA measurements. The EfficientNetB0 feature extractor outperformed other models, demonstrating strong performance on the training set (<span><math><mtext>R</mtext><mo>=</mo><mn>0.96</mn></math></span>, R<span><math><mmultiscripts><mrow><mo>=</mo></mrow><mprescripts></mprescripts><none></none><mrow><mn>2</mn></mrow></mmultiscripts><mn>0.93</mn></math></span>) and achieving an MAE of <span><math><msup><mrow><mn>6.13</mn></mrow><mrow><mo>∘</mo></mrow></msup></math></span> and RMSE of <span><math><msup><mrow><mn>7.5</mn></mrow><mrow><mo>∘</mo></mrow></msup></math></span> on the test set. In terms of scoliosis severity classification, it achieved high precision (84.62%) and specificity (95.65% for mild cases and 82.98% for severe cases), highlighting its clinical applicability in AIS management. The regression-based approach using the EfficientNetB0 as a feature extractor presents a significant advancement for accurately determining CA from ST scans, offering a promising tool for improving scoliosis severity categorization and management in adolescents.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"140 ","pages":"Article 104332"},"PeriodicalIF":1.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863885","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 new network structure for Parkinson's handwriting image recognition","authors":"Xiao Jiang ,&nbsp;Haibin Yu ,&nbsp;Jiayu Yang ,&nbsp;Xiaoli Liu ,&nbsp;Zhu Li","doi":"10.1016/j.medengphy.2025.104333","DOIUrl":"10.1016/j.medengphy.2025.104333","url":null,"abstract":"<div><div>Parkinson's disease (PD) remains a condition without a cure, though its early manifestations can be managed effectively by medical professionals. This underscores the significance of early detection of PD. It has been widely demonstrated that handwriting analysis is a promising avenue for early PD diagnosis. In recent research, there has been a pivot towards leveraging artificial intelligence (AI) technologies for analyzing handwriting images to aid in diagnosing the disease. This study introduces an innovative network architecture specifically designed to capture the nuances of tremor and irregular spacing characteristic of PD patients' handwriting. By incorporating an attention mechanism, this network is capable of prioritizing different areas within the handwriting feature map, according to their diagnostic relevance. This approach significantly enhances the accuracy of detecting PD through handwriting analysis, with our model achieving an impressive mean accuracy rate of 96.5 %. When compared to traditional convolutional neural networks, our attention-based continuous convolutional network model demonstrates a substantial increase in diagnostic precision.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104333"},"PeriodicalIF":1.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833284","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":"Physiological consequences of half-embedded drug-eluting stent on coronary drug-transport: A two-species drug delivery simulation","authors":"Ramprosad Saha ,&nbsp;Akash Pradip Mandal ,&nbsp;Prashanta Kumar Mandal","doi":"10.1016/j.medengphy.2025.104334","DOIUrl":"10.1016/j.medengphy.2025.104334","url":null,"abstract":"<div><div>Drug-eluting stent (DES) is commonly utilized to open blocked arteries and reduce in-stent restenosis (ISR) brought on by the implantation of bare-metal stent (BMS). The current mathematical model sheds light on investigating the physiological effects of different clinical parameters on drug distribution and retention within the artery wall. A two-dimensional (2D) axisymmetric cylindrical model of a typical Palmaz stent having circular struts coated with a bio-durable polymer is half-embedded in the tissue. This investigation considers two distinct drug phases - the unbound and bound phases - in the artery wall. The artery wall is considered a porous medium, and the Brinkman equation governs the interstitial fluid (ISF) flow within it. An unsteady convection-diffusion-reaction mechanism governs unbound drug transport, while that of bound drug is solely controlled by an unsteady chemical reaction. The drug delivery system also includes a reversible equilibrium mechanism to maintain the chemical reaction between the drug molecules and the receptors. Drug and momentum transport equations are solved using the Marker-and-Cell (MAC) method in staggered grid setups with appropriate initial and boundary conditions. According to simulations, there is going to be a bi-phasic decrease in unbound drug in the artery wall with a larger binding-on constant (<em>ψ</em>), and the concentration of both drug forms and the area under concentration decrease as the Peclet number (<em>Pe</em>) increases. Additionally, as the equilibrium association constant (<span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>e</mi><mi>q</mi></mrow></msub></math></span>) increases, the concentration of the unbound drug decreases, but the tendency for the bound drug is reversed. The sensitivity of some significant parameters has been examined in a thorough sensitivity study. Our results are in excellent agreement with the findings available in the literature.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104334"},"PeriodicalIF":1.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830138","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":"Predicting ground reaction forces and center of pressures from kinematic data in crutch gait based on LSTM","authors":"Xinyu Guan ,&nbsp;Hanyu Chen ,&nbsp;Yali Liu ,&nbsp;Ziwei Zhang ,&nbsp;Linhong Ji","doi":"10.1016/j.medengphy.2025.104338","DOIUrl":"10.1016/j.medengphy.2025.104338","url":null,"abstract":"<div><div>Crutches are of extensive applications in the field of rehabilitation. Comprehensively analyzing the ground reaction forces (GRFs) on both crutches and feet can evaluate the patients’ walking function recovery. Given more force platforms are needed in clinical evaluation for the crutch gait than the normal gait pattern and the resulting high cost, this research proposes a method to predict both ground and foot GRFs during walking with crutches, using kinematic information from motion capture trials. We collected force and motion data, built a musculoskeletal model in Opensim, and computed joint angles and moments of crutch gait. Different Artificial Neural Networks (ANN), including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory network (LSTM) were established to test their predictive ability using Leave-One-Subject-Out(LOSO) cross validation method. LSTM model showed the strongest agreement, with <em>r</em> = 0.961±0.050 and nRMSE=13.8 % in the vertical direction of the left foot. The LSTM model was more accurate than the CNN model and more robust than the MLP model in this component. In average of different directions, LSTM model has <em>r</em> = 0.656±0.362 and nRMSE=30.3 %. Further verification of the prediction was executed by computing joint moments. The LSTM model showed great application prospects in crutch gait GRF analysis.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104338"},"PeriodicalIF":1.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823813","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":"AMRM: Attention-based mask reconstruction module for multi-classification of breast cancer histopathological images","authors":"Yanguang Cai ,&nbsp;Xiang Chen ,&nbsp;Changle Guo","doi":"10.1016/j.medengphy.2025.104335","DOIUrl":"10.1016/j.medengphy.2025.104335","url":null,"abstract":"<div><div>Nowadays, breast cancer is a leading cause of cancer-related mortality among women globally. Approximately 10% to 15% of breast cancer patients fail to undergo timely screening, resulting in a missed opportunity for optimal treatment. Computer-aided diagnosis (CAD) systems have been used successfully in breast cancer diagnosis. Nevertheless, current systems have encountered difficulties in achieving a high degree of accuracy, with the majority of research efforts focusing on the binary classification that distinguishes benign from malignant. Different subtypes of breast cancer require different targeted therapeutic approaches. Therefore, the precise classification of the breast cancer subtype has a major impact on treatment decisions. To improve the accuracy of breast cancer multi-classification, a novel Attention-based Mask Reconstruction Module (AMRM) is proposed to improve the performance of the model. AMRM extracts features from breast cancer histopathological images through the attention module and then performs mask reconstruction to generate reconstructed features. These reconstructed features were used in a multi-classification task to accurately classify histopathological images of breast cancer. AMRM enables the network to effectively identify background and foreground in histopathological images, reduce background interference, improve adaptability to background changes, align the features extracted by the model with the pathologist's expectations, and improve classification accuracy. Results from experiments conducted on the BreakHis dataset show that the inclusion of AMRM resulted in a significant improvement in multi-classification accuracy for the AlexNet, VGG11, ResNet-50 and Data-efficient Image Transformer (DeiT) models, reaching 88.48%, 93.40%, 96.49% and 94.10% respectively. Compared to the baseline model, accuracy increased by 8.28%, 2.11%, 1.27% and 1.26% respectively, demonstrating a significant improvement.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104335"},"PeriodicalIF":1.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815025","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":"Enhancing polyp classification: A comparative analysis of spatio-temporal techniques","authors":"Aditi Jain ,&nbsp;Saugata Sinha ,&nbsp;Srijan Mazumdar","doi":"10.1016/j.medengphy.2025.104336","DOIUrl":"10.1016/j.medengphy.2025.104336","url":null,"abstract":"<div><div>Colorectal cancer (CRC) is a major health concern, ranking as the third deadliest cancer globally. Early diagnosis of adenomatous polyps which are pre-cancerous abnormal tissue growth, is crucial for preventing CRC. Artificial intelligence-assisted narrow-band imaging colonoscopy can significantly increase the accuracy of polyp characterization during the endoscopy procedure. This study presents a comprehensive comparative analysis of the performances of three different deep architectures for incorporating temporal information alongside spatial features for colon polyp classification. We employed three different models namely, time-distributed 2D CNN-LSTM, 3D CNN, and hybrid 3D CNN-ConvLSTM2D model and evaluated their performance of polyp characterization using a real-world clinical dataset of NBI colonoscopy videos of 64 different polyps from 60 patients in India. Additionally, cross-dataset validation on a publicly available dataset demonstrated the generalizability and robustness of the proposed model. The 3D CNN-ConvLSTM2D model outperforms the other two in terms of all evaluation metrics. Notably, it achieved a mean NPV of 92%, surpassing the minimum NPV threshold set by PIVI guidelines for reliable polyp diagnosis which demonstrates its suitability for real-world applications. The performance of the proposed deep architectures is also compared with some existing methods proposed by other researchers, and 3D CNN-ConvLSTM2D model demonstrates significant improvements in both NPV and overall performance metrics in comparison with the other existing methods, while also effectively reducing false positives. This study demonstrates the effectiveness of employing spatiotemporal features for accurate polyp classification. To the best of our knowledge, this is the first study performed, using exclusively NBI polyp dataset, to investigate the effectiveness of spatiotemporal information for polyp classification.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104336"},"PeriodicalIF":1.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815026","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":"Impact of transforaminal lumbar interbody fusion on rod load: a comparative biomechanical analysis between a cadaveric instrumentation and simulated bone fusion","authors":"Maximilian Heumann ,&nbsp;Chencheng Feng ,&nbsp;Lorin M. Benneker ,&nbsp;Maarten Spruit ,&nbsp;Christian Mazel ,&nbsp;Jan Buschbaum ,&nbsp;Boyko Gueorguiev ,&nbsp;Manuela Ernst","doi":"10.1016/j.medengphy.2025.104339","DOIUrl":"10.1016/j.medengphy.2025.104339","url":null,"abstract":"<div><h3>Background</h3><div>Recent research has demonstrated the potential of implant load monitoring to assess posterolateral spinal fusion in a sheep model. This study investigated whether such a system could monitor bone fusion after interbody fusion surgery by biomechanically testing of human cadaveric lumbar spines in two states: following a transforaminal lumbar interbody fusion (TLIF) procedure and after simulating bone fusion.</div></div><div><h3>Methods</h3><div>Eight human cadaveric spines underwent a TLIF procedure at L4-L5. An implantable sensor system was attached to one rod, while two strain gauges were attached to the contralateral rod (dorsally and ventrally) to derive implant load changes during unconstrained flexion-extension (FE), lateral bending (LB) and axial rotation (AR) motion. The specimens were retested after simulating bone fusion at L4-L5. Range of motion (ROM) of L4-L5 was measured during each loading mode.</div></div><div><h3>Results</h3><div>ROM decreased in the simulated bone fusion state in all loading directions (p ≤ 0.002). Compared to the TLIF motion, the remnant motion after simulated fusion was 53 ± 21 % in FE, 40 ± 12 % in LB, and 49 ± 16 % in AR. In both states, measured strain on the posterior instrumentation was highest during LB motion. All sensors detected a significant decrease in load-induced rod strain after simulated bone fusion in LB (p ≤ 0.002). The strain measured by the implantable strain sensor, the dorsal strain gauge, and the ventral strain gauge decreased to 49 ± 12 %, 49 ± 17 %, and 54 ± 17 %, respectively.</div></div><div><h3>Conclusion</h3><div>Rod load measured via strain sensors can monitor fusion progression after a TLIF procedure when measured during isolated LB of the lumbar spine. This study provides the basis for further development and understanding of in vivo implant load data.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104339"},"PeriodicalIF":1.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838111","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":"DDUM: Deformable Dilated U-structure Module for coronary stenosis detection","authors":"Chenru Wang ,&nbsp;Zirui Chen ,&nbsp;Muyao Li ,&nbsp;Haoran Yin ,&nbsp;Saijie Zhou ,&nbsp;Jingliang Zhang ,&nbsp;Xueying Zeng ,&nbsp;Qing Zhang","doi":"10.1016/j.medengphy.2025.104337","DOIUrl":"10.1016/j.medengphy.2025.104337","url":null,"abstract":"<div><div>Deep learning methods are increasingly popular in assisting physicians with diagnosing coronary artery disease and reducing errors caused by subjective judgment. However, accessing and labeling medical imaging data, especially coronary angiography data, is challenging. Consequently, models trained on such datasets often exhibit low accuracy, high false-positive rates, and limited generalization capabilities. We propose a Deformable Dilatable U-structure Module that can specialize a common network for coronary stenosis detection and enhance its generalization ability. Experiments demonstrate that our proposed module significantly improves the performance of various models. When applying DDUM to a model with ResNet50 as the backbone and faster R-CNN as the detector, the mean average precision increases from 33.76 to 42.39, a 25.56% improvement. Additionally, we show that DDUM enhances the network's generalization ability through transfer learning experiments. This module can improve the network's accuracy for stenosis detection and enhance the generalization ability of the original model. Fine-tuning reduces training costs and ensures that the model can be easily adapted and deployed across different devices.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104337"},"PeriodicalIF":1.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814968","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":"Mixture density knowledge distillation in super-resolution reconstruction of mri medical images","authors":"Xiangchun Yu ,&nbsp;Ningning Zhou ,&nbsp;Jian Zheng ,&nbsp;Miaomiao Liang ,&nbsp;Liujin Qiu ,&nbsp;Qing Xu","doi":"10.1016/j.medengphy.2025.104330","DOIUrl":"10.1016/j.medengphy.2025.104330","url":null,"abstract":"<div><h3>Motivation</h3><div>MRI medical image reconstruction frequently suffers from a smoothness bias, resulting in sub-optimal multi-valued mapping fitting. Mixture Density Networks (MDNs) offer a potential solution by modeling multi-valued functions via multiple components. However, numerical instability in MDNs undermines their performance. Moreover, the super-resolution task is inherently difficult due to its ill-posed nature.</div></div><div><h3>Description</h3><div>To overcome these challenges, we introduce MixtUre densiTy knowlEdge Distillation (MUTED), a novel framework for super-resolution reconstruction. MUTED integrates the MDN module to mitigate boundary blurring, addresses MDN's numerical instability via an adversarial approach, and employs regularization derived from knowledge distillation to handle the ill-posed problem.</div></div><div><h3>Results</h3><div>Extensive experiments on the IXI and BraTS21 datasets show that our MUTED framework effectively produces high-quality reconstructions. It outperforms existing methods in handling boundary blurring and numerical instability, as evidenced by experimental and visualization results.</div></div><div><h3>Conclusion</h3><div>MUTED surpasses state-of-the-art (SOTA) methods with a reduced computational cost and outperforms competing knowledge distillation methods. By addressing numerical instability and leveraging the regularization constraint, MUTED offers a robust solution for high-quality image reconstruction. Furthermore, the aleatoric uncertainty formulated by the MDN serves to reveal sharpened boundaries. This, in turn, effectively facilitates the efficient enhancement of the super-resolution reconstruction quality.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"139 ","pages":"Article 104330"},"PeriodicalIF":1.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785452","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}