Physical and Engineering Sciences in Medicine最新文献_第10页

Education note: cultivating assessment and feedback for learning in our training hospital. 教育说明：在我们的培训医院培养学习评估和反馈。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-03-23 DOI: 10.1007/s13246-026-01721-7

Rebecca Day, Louise Beckingsale

{"title":"Education note: cultivating assessment and feedback for learning in our training hospital.","authors":"Rebecca Day, Louise Beckingsale","doi":"10.1007/s13246-026-01721-7","DOIUrl":"10.1007/s13246-026-01721-7","url":null,"abstract":"High-value feedback is essential for the successful development of medical physics trainees (registrars) as they complete their clinical training. In this education note, we present an overview of a small education intervention we implemented in our hospital to encourage feedback conversations, one of the principles of high-value feedback. Our intervention was based on the concepts of 'assessment for learning' and 'programmatic assessment'. This intervention encourages registrars to take ownership of the feedback process associated with each learning outcome assessment. Registrars are encouraged to complete and document a self-assessment of their performance, before seeking feedback from their trainer. We modified an assessment template to support this process. This new approach appears to have resulted in higher quality feedback conversations between registrars and trainers. Additionally, trainers have benefited from the registrars completing the majority of the assessment template, thus reducing their workload. Whilst this approach has increased the workload for registrars, we believe this has benefits for their learning. We hope other centres can learn from our experience and may consider how they can adopt a similar process for their setting.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1105-1110"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new model based on multi-axis vision transformer for chondromalacia patella diagnosis in magnetic resonance scans. 基于多轴视觉变压器的髌骨软骨软化症磁共振诊断新模型。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1007/s13246-026-01707-5

Semih Demirel, Okan Demirtaş, Sümeyra Kuş Ordu, Ömer Kazcı, Habip Eser Akkaya, Oktay Yıldız

{"title":"A new model based on multi-axis vision transformer for chondromalacia patella diagnosis in magnetic resonance scans.","authors":"Semih Demirel, Okan Demirtaş, Sümeyra Kuş Ordu, Ömer Kazcı, Habip Eser Akkaya, Oktay Yıldız","doi":"10.1007/s13246-026-01707-5","DOIUrl":"10.1007/s13246-026-01707-5","url":null,"abstract":"A degenerative disease of the patellofemoral joint cartilage, chondromalacia patella (CMP) often results in anterior knee discomfort and functional disability. Determining the best course of therapy and stopping the progression of the disease depend on an accurate and timely diagnosis. In this work, we provide a deep learning architecture based on transformers for the classification of chondromalacia patella using magnetic resonance imaging (MRI) data. We assessed transformer-based designs including Multi-Axis Vision Transformer (MaxViT), Vision Transformer (ViT), and Swin Transformer in addition to convolutional neural network (CNN) based models like Google Network (GoogLeNet), Residual Network 18 (ResNet18), and Mobile Network v2 (MobileNetV2). We evaluated the models' ability to differentiate between cases of chondromalacia patella and normal cases. With an accuracy of 0.9817, precision of 0.9821, recall of 0.9817, and F1-score of 0.9818, Multi-Axis Vision Transformer outperformed all other models on the testing dataset.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"863-879"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization. 用于剂量优化的新型aec平板探测器的基本性能和临床用途。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-01-27 DOI: 10.1007/s13246-026-01705-7

Sho Maruyama, Hiroki Saitou, Nao Koyama

{"title":"Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization.","authors":"Sho Maruyama, Hiroki Saitou, Nao Koyama","doi":"10.1007/s13246-026-01705-7","DOIUrl":"10.1007/s13246-026-01705-7","url":null,"abstract":"The demand for bedside radiography is increasing due to critical clinical needs, including infection control and the limited mobility of severely ill patients. However, radiation dose adjustment in these settings remains heavily reliant on the expertise and experience of radiographers. To address this issue, a novel flat panel detector (FPD) integrated with an automatic exposure control (AEC) system has been developed. This study aims to experimentally evaluate the fundamental performance of this system and clarify its clinical utility, including its potential limitations. The dependency of the AEC performance on object thickness and tube voltage was investigated using acrylic phantoms. To simulate clinical scenarios, the AEC response was examined using a chest phantom. Additionally, the effects of source-to-image distance and oblique X-ray incidence on the AEC performance were also evaluated using a quality-control test device. Our results elucidated the behavior of the exposure index (EI) and image quality under varying tube voltage and object thickness. In clinical conditions, the introduction of the AEC system significantly reduced EI, confirming its potential for effective dose management. Multiple factors were identified that influence both the AEC response and image quality, such as sensor positioning, imaging distance, and beam angle. These findings demonstrate that the AEC-equipped FPD system maintains consistent image quality while effectively reducing the radiation dose under various simulated imaging conditions. Our results also underscore the importance of accounting for environmental factors that affect dose control and image characteristics, highlighting the need for practical adjustment in routine clinical operation.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"837-847"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054610","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

Bridging precision and practice: dual validation of a high-resolution detector array for beam profiling and patient QA in robotic radiosurgery. 桥接精度和实践：高分辨率探测器阵列在机器人放射外科的光束剖面和患者质量保证的双重验证。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-02-24 DOI: 10.1007/s13246-026-01716-4

Sandeep Singh, Supratik Sen, Abhay Kumar Singh, Dipesh, Manindra Bhushan, Benoy Kumar Singh, Sarthak Tandon, Munish Gairola

{"title":"Bridging precision and practice: dual validation of a high-resolution detector array for beam profiling and patient QA in robotic radiosurgery.","authors":"Sandeep Singh, Supratik Sen, Abhay Kumar Singh, Dipesh, Manindra Bhushan, Benoy Kumar Singh, Sarthak Tandon, Munish Gairola","doi":"10.1007/s13246-026-01716-4","DOIUrl":"10.1007/s13246-026-01716-4","url":null,"abstract":"This study assessed a high-resolution ionisation chamber-based PTW 1600SRS detector array (array) for beam profile analysis and patient-specific quality assurance (PSQA) in CyberKnife (CK) stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT). The goal was to determine its suitability for small fields and non-isocentric delivery, which is unique to robotic platforms. Detector performance was examined for dose linearity, reproducibility, beam profiles, output factors, dose-rate dependence, and verification of the Iris collimator field size. Results were benchmarked against diode-based commissioning data. Clinical applicability was tested by retrospectively verifying 20 intracranial SRS and 20 extracranial SBRT plans using gamma analysis with criteria ranging from 3%/3 mm to 1%/1 mm, as well as 4%/1 mm. The detector showed strong dose linearity (R2 = 0.999) and stable reproducibility. Beam profiles matched commissioning values within 0.5 mm, and output factors agreed within 2% for most collimators, with a maximum deviation of 3% at 5 mm. Dose-rate variation remained within 2.5% across relevant SADs. Iris collimator field sizes were consistent with reference measurements. Clinical validation achieved high passing rates, all with tolerance limit of > 95%. It enables accurate beam characterization and reliable PSQA in CK treatments. This work provides the first combined evaluation of beam analysis and clinical validation for this detector on a robotic radiosurgery system, supporting its routine use in small-field quality assurance.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"771-783"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147285475","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

Equivalence of analog and digital high-frequency electrocardiogram: validating Sydäntek for ischemia detection. 模拟和数字高频心电图的等效性：验证Sydäntek用于缺血检测。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2025-12-29 DOI: 10.1007/s13246-025-01690-3

Aishwarya Srinivasan, K Vijayalakshmi, Sathish Kumar, Poulami Roy, V J Karthikeyan

{"title":"Equivalence of analog and digital high-frequency electrocardiogram: validating Sydäntek for ischemia detection.","authors":"Aishwarya Srinivasan, K Vijayalakshmi, Sathish Kumar, Poulami Roy, V J Karthikeyan","doi":"10.1007/s13246-025-01690-3","DOIUrl":"10.1007/s13246-025-01690-3","url":null,"abstract":"High-frequency electrocardiography (HF-ECG) enhances ischemia detection by capturing microvolt-level changes in the QRS complex; however, clinical adoption requires validating digital systems against Analog standards. We recorded HF-ECG signals simultaneously from 12 healthy subjects (84 beats total) using a five-stage Analog reference (100-500 Hz band-pass, gold connectors) and Sydäntek's 10× capacitive sensors; both outputs were digitized using a Texas Instruments ADS1298. Signals underwent 10× amplification with a low-noise op-amp, the Analog output was scaled to match, and data were processed in PulseTek™ and stored in PulseVault™. Root mean square (RMS), Amplitude, Kurtosis, and Frequency content were compared using Bland-Altman analysis (Analog as the reference); values reported reflect pre-amplified measurements in microvolts (µV). Mean differences between the Analog setup and Sydäntek fell within the 95% limits of agreement (LOA): RMS, 6.39 µV (- 49.74 to 62.52 µV); amplitude, 1.82 µV (- 57.09 to 60.73 µV); kurtosis, 1.93 (- 5.13 to 1.54); and frequency, 2.1 Hz (- 5.8 to 6.2 Hz), all within a 5% clinical tolerance when scaled 10× (~ 10-20 mV). Sydäntek matched analog fidelity, with frequency peaks near ~ 150 Hz, indicating digital HF-ECG performance equivalent to that of the Analog system on key metrics. Its wearable design and cloud integration provide a portable, reliable alternative for ischemia detection with broader clinical applicability.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"631-637"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851175","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

Improved schizophrenia detection using structural MRI with multi-scale transform and deep orthogonal features. 基于多尺度变换和深度正交特征的结构MRI改进精神分裂症检测方法。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-02-10 DOI: 10.1007/s13246-026-01702-w

Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore

引用次数: 0

Dependence of motion artifacts on the starting angle of projection data collection in a nonhelical volume scan of a 320-row CT. 320排CT非螺旋体扫描中运动伪影对投影数据采集起始角度的依赖性。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-03-09 DOI: 10.1007/s13246-026-01719-1

Yuho Kikuchi, Kazuhiro Sato, Toshimitsu Sato, Makoto Ohba, Koji Suzuki

引用次数: 0

Publisher Correction to: In radiation oncology, the best way to maintain patient safety when implementing cutting edge technologies such as AI-based and real-time adaptive techniques is through prospective hazard analysis. 在放射肿瘤学中，在实施尖端技术（如基于人工智能和实时自适应技术）时，维护患者安全的最佳方法是通过前瞻性危害分析。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 DOI: 10.1007/s13246-026-01766-8

Jonathan Hindmarsh, Nicole Jessop, Clive Baldock

引用次数: 0

Correction to: A study on using custom 3D-printed tongue bites for radiotherapy patients with oral tongue carcinoma. 更正：定制3d打印舌咬用于放疗口腔舌癌患者的研究。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 DOI: 10.1007/s13246-026-01748-w

Muhammad Furqan Zulqurnain, Haris Arif, Misbah Batool, Nouman Amjad, Robin Hill

引用次数: 0

Hybrid imaging-clinical model for predicting microvascular invasion in hepatocellular carcinoma using deep learning-derived features from CT. 基于CT深度学习特征预测肝细胞癌微血管侵袭的混合影像-临床模型。

IF 2.4 4区医学

Physical and Engineering Sciences in Medicine Pub Date : 2026-06-01 Epub Date: 2026-01-19 DOI: 10.1007/s13246-025-01689-w

Shidi Miao, Mingxuan Wang, Qi Dong, Qifan Xuan, Le Liu, Mengzhuo Sun, Yuyang Jiang, Yuxin Jiang, Ruitao Wang, Qiujun Wang, Zengyao Liu, Xuemei Ding, Haipeng Jin

{"title":"Hybrid imaging-clinical model for predicting microvascular invasion in hepatocellular carcinoma using deep learning-derived features from CT.","authors":"Shidi Miao, Mingxuan Wang, Qi Dong, Qifan Xuan, Le Liu, Mengzhuo Sun, Yuyang Jiang, Yuxin Jiang, Ruitao Wang, Qiujun Wang, Zengyao Liu, Xuemei Ding, Haipeng Jin","doi":"10.1007/s13246-025-01689-w","DOIUrl":"10.1007/s13246-025-01689-w","url":null,"abstract":"The role of adipose tissue in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains unclear. This study proposes a method that integrates deep learning and machine learning techniques to investigate the role of adipose tissue in identifying MVI status in HCC patients. We collected enhanced Computed Tomography images from 517 HCC patients across two independent centers, dividing them into a training set, validation set, and test set. The model was constructed using adipose and tumor deep learning features along with clinical features, and the features were input into a classifier for prediction. The model performance was evaluated using the area under the curve(AUC), decision curve analysis, scatter plots, and box plots. Furthermore, we compared the model's performance with that of three radiologists. After incorporating the adipose tissue modality, the venous-phase AUC reached 0.866 (95% CI 0.803-0.920), while the arterial-phase AUC was 0.864 (95% CI 0.792-0.920). The inclusion of the adipose tissue modality provided significant value for clinical diagnosis, which was further validated through visualization analysis. Using predicted labels for grouping, it shows that the overall survival of the high-risk group was significantly lower than that of the low-risk group. Comparative analysis showed that the predictive performance of the model surpassed that of radiologists. Univariate analysis identified the adipose region as a risk factor for predicting MVI status. We developed a hybrid multimodal model that performed comparably to radiologists' assessments. The inclusion of the adipose tissue modality enhanced the accuracy of MVI diagnosis.","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"591-601"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999418","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