Physical and Engineering Sciences in Medicine最新文献

{"title":"A multi-institutional survey on radiosurgery for multiple brain metastases in Latin America.","authors":"José Alejandro Rojas-López, Mariluz De Ornelas, Miguel Ángel Chesta, Carlos Daniel Venencia","doi":"10.1007/s13246-025-01652-9","DOIUrl":"https://doi.org/10.1007/s13246-025-01652-9","url":null,"abstract":"<p><p>Disparity in the delivery of stereotactic radiosurgery (SRS) treatment for multiple intracranial lesions has been observed in six countries in Latin America. Moreover, no consensus exists on tumor margins for these treatments. This work aims to collect multi-institutional data on the SRS technique for multiple brain lesions, based on clinical practice across various centers. A Google Forms survey was developed to analyze and report the data. The anonymous survey was distributed to specialists, including neurosurgeons, radiation oncologists, medical physicists, and dosimetrists via professional societies in Latin America. A total of 23 from six Latin American countries participated. Among them, 52.1% treat 1 to 25 cases of SRS of brain metastases annually, and 69.5% operate with a single treatment machine. The most commonly used imaging system was cone beam computed tomography (78.2%), followed by ExacTrac™ (34.7%). However, responses regarding machine uncertainties and margin assignment revealed significant variability. Multi-institutional disparities in Latin America exist in technical and clinical aspects of SRS for multiple brain lesions, largely due to differences in technology availability and a lack of comprehensive understanding of the sources of uncertainty.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193564","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":"Accuracy of surface-guided radiotherapy-based positioning and surface tracking in breast radiotherapy using a thermo-optical camera system.","authors":"Hiroki Katayama, Yosuke Takahashi, Motonori Kitaoka, Hiroki Kawasaki, Takashi Tanii, Yayoi Taniguchi, Masato Tsuzuki","doi":"10.1007/s13246-025-01649-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01649-4","url":null,"abstract":"<p><p>This study aimed to evaluate the accuracy of surface-guided radiotherapy (SGRT)-based positioning and surface tracking in breast radiotherapy using the ExacTrac Dynamic (ETD). We retrospectively evaluated 16 patients who underwent breast cancer treatment. All patients were positioned using SGRT with ETD under free-breathing conditions. Orthogonal kilovoltage (kV) image acquisition was then acquired using the linear accelerators' kV imagers and registered to digitally reconstructed radiographs. For surface tracking, vertical breast surface motion waveforms obtained with ETD were compared with those captured using an electronic portal imaging device (EPID) during beam delivery. The agreement between ETD and EPID in terms of vertical breast surface displacement was evaluated using the cross-correlation coefficient. For patient positioning, the mean ± standard deviation (SD) of X-ray shift values (mm) were - 2.8 ± 2.6 (vertical), - 2.2 ± 4.5 (longitudinal), and 0.3 ± 2.1(lateral). The percentage of X-ray shift < 5 mm was 82% (vertical), 66% (longitudinal), and 92% (lateral). The setup margins (mm) were 5.4 (vertical), 9.0 (longitudinal), and 3.8 (lateral). For surface tracking, the mean ± SD of the cross-correlation coefficient was 0.93 ± 0.02, indicating a high correlation between the ETD and EPID waveforms. The mean amplitude difference between waveforms obtained by ETD and EPID was 0.46 mm. SGRT-based positioning with ETD may result in errors exceeding 5 mm relative to the treatment planning position in the longitudinal direction; combining it with image-guided radiotherapy is therefore recommended. Surface tracking with ETD demonstrated high tracking accuracy in tracking human body surface temperature distribution.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193550","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":"FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.","authors":"Kavita Bhatt, N Jayanthi, Manjeet Kumar","doi":"10.1007/s13246-025-01644-9","DOIUrl":"https://doi.org/10.1007/s13246-025-01644-9","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) is a chronic neurological disorder that impairs the cognitive and behavioral abilities of older people. Early detection and treatment are crucial for minimizing the progression of the disease. Electroencephalogram (EEG) makes it possible to investigate the brain activities linked to various forms of disabilities experienced by individuals with AD. Nevertheless, the EEG signals are non-linear and non-stationary in nature making it difficult to retrieve the concealed information from the EEG signals. Therefore, a Fourier Decomposition Method (FDM) and Hilbert Transform (HT) based EEG signals analysis (FHESA) method is developed in this paper for the automated detection of AD. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The proposed FHESA method is divided into three primary stages. The first stage deals with the decomposition of the EEG signals into a finite number of Fourier Intrinsic Band Functions (FIBFs). In the second stage, HT is applied to all FIBFs to obtain instantaneous amplitude, frequency, and phase, that are then used to construct feature vectors. In the last stage, various Machine Learning (ML) algorithms are used to classify these feature vectors for efficient AD detection. Two distinct data sets are employed to assess the effectiveness of the proposed FHESA method. The outcome demonstrates that with dataset-I and dataset-II, the proposed methodology can detect AD with 98% and 99% accuracy, respectively. The performance of the proposed FHESA method is compared to other state-of-the-art methods used for AD detection. The promising results show that the proposed FHESA method can assist neurological experts in identifying and utilizing EEG signals for AD detection.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193659","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 image analysis to build patient-specific models of acute ischemic stroke patients.","authors":"Virginia Fregona, Ilaria Bottini, Sara Barati, Amedeo Cervo, Antonio Macera, Ghil Schwarz, Guglielmo Pero, Mariangela Piano, Gabriele Dubini, Jose Felix Rodriguez Matas, Giulia Luraghi, Francesco Migliavacca","doi":"10.1007/s13246-025-01646-7","DOIUrl":"https://doi.org/10.1007/s13246-025-01646-7","url":null,"abstract":"<p><p>Mechanical thrombectomy (MT) is an emergency treatment for acute ischemic stroke (AIS) to remove a clot occluding a large cerebral vessel. Histological analysis on retrieved thrombi have shown that they are mainly composed of red blood cells (RBCs), platelets and fibrin, and the outcome of MT appears to be influenced by clot composition. Therefore, being able to predict clot composition from routine medical images used for AIS diagnosis could support the choice of interventional strategy. Along with that, finite element simulations of the MT procedure can help provide insights into the impact of the procedural choices, the vessels morphology and the clot characteristics on the MT outcome. To achieve this, a realistic representation of the involved structures is necessary. In this context, this work aimed to (i) develop a methodology for the analysis of routine radiological images aiming at inferring information about clot characteristics (position, length, and composition) and (ii) develop a semi-automatic pipeline to position the clot in the patient-specific reconstructed geometry to build a patient-specific model which could be the starting point for the in silico replica of the MT procedure. However, image analysis alone could not distinguish between white and mixed clots, while a distinction between red and non-red clots was possible. Consequently, histological analyses were used to assign the clot composition, and thus the mechanical properties, in the positioning simulation. The resulting patient-specific model showed a strong similarity with pre-interventional clinical images.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193599","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":"SAFNet: a spatial adaptive fusion network for dual-domain undersampled MRI reconstruction.","authors":"Yingjie Huo, Hongyuan Zhang, Dan Ge, Ziliang Ren","doi":"10.1007/s13246-025-01628-9","DOIUrl":"https://doi.org/10.1007/s13246-025-01628-9","url":null,"abstract":"<p><p>Undersampled magnetic resonance imaging (MRI) reconstruction reduces scanning time while preserving image quality, improving patient comfort and clinical efficiency. Current parallel reconstruction strategies leverage k-space and image domains information to improve feature extraction and accuracy. However, most existing dual-domain reconstruction methods rely on simplistic fusion strategies that ignore spatial feature variations, suffer from constrained receptive fields limiting complex anatomical structure modeling, and employ static frameworks lacking adaptability to the heterogeneous artifact profiles induced by diverse undersampling patterns. This paper introduces a Spatial Adaptive Fusion Network (SAFNet) for dual-domain undersampled MRI reconstruction. SAFNet comprises two parallel reconstruction branches. A Dynamic Perception Initialization Module (DPIM) in each encoder enriches receptive fields for multi-scale information capture. Spatial Adaptive Fusion Modules (SAFM) within each branch's decoder achieve pixel-wise adaptive fusion of dual-domain features and incorporate original magnitude information, ensuring faithful preservation of intensity details. The Weighted Shortcut Module (WSM) enables dynamic strategy adaptation by scaling shortcut connections to adaptively balance residual learning and direct reconstruction. Experiments demonstrate SAFNet's superior accuracy and adaptability over state-of-the-art methods, offering valuable insights for image reconstruction and multimodal information fusion.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139027","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":"3D CoAt U SegNet-enhanced deep learning framework for accurate segmentation of acute ischemic stroke lesions from non-contrast CT scans.","authors":"Manas K Nag, Anup K Sadhu, Samiran Das, Chandan Kumar, Sandeep Choudhary","doi":"10.1007/s13246-025-01626-x","DOIUrl":"https://doi.org/10.1007/s13246-025-01626-x","url":null,"abstract":"<p><p>Segmenting ischemic stroke lesions from Non-Contrast CT (NCCT) scans is a complex task due to the hypo-intense nature of these lesions compared to surrounding healthy brain tissue and their iso-intensity with lateral ventricles in many cases. Identifying early acute ischemic stroke lesions in NCCT remains particularly challenging. Computer-assisted detection and segmentation can serve as valuable tools to support clinicians in stroke diagnosis. This paper introduces CoAt U SegNet, a novel deep learning model designed to detect and segment acute ischemic stroke lesions from NCCT scans. Unlike conventional 3D segmentation models, this study presents an advanced 3D deep learning approach to enhance delineation accuracy. Traditional machine learning models have struggled to achieve satisfactory segmentation performance, highlighting the need for more sophisticated techniques. For model training, 50 NCCT scans were used, with 10 scans for validation and 500 scans for testing. The encoder convolution blocks incorporated dilation rates of 1, 3, and 5 to capture multi-scale features effectively. Performance evaluation on 500 unseen NCCT scans yielded a Dice similarity score of 75% and a Jaccard index of 70%, demonstrating notable improvement in segmentation accuracy. An enhanced similarity index was employed to refine lesion segmentation, which can further aid in distinguishing the penumbra from the core infarct area, contributing to improved clinical decision-making.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126342","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":"Repeatability of diffusion and perfusion MRI derived radiomic features in glioblastoma: a test-retest study.","authors":"Philip Martin, Lois Holloway, Peter Metcalfe, Eng-Siew Koh, Farhannah Aly, Edward Chan, Caterina Brighi","doi":"10.1007/s13246-025-01613-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01613-2","url":null,"abstract":"<p><p>An understanding of the repeatability of imaging biomarkers is key to their implementation as clinical tools. In this study we calculate the repeatability and inter-correlation of radiomic features derived from quantitative MRI (qMRI) of Glioblastoma (GBM) patients and assess the effect of image standardisation methods on these factors. We analysed scan-rescan Diffusion Weighted MR Images (DWI) and Dynamic Contrast Enhanced MR Images (DCE) from 36 GBM patients obtained from The Cancer Imaging Archive (TCIA). These included 17 patients, from the QIN-GBM-Treatment-Response patient cohort, scanned post surgery and prior to chemo-radiation therapy and 19 patients, from the RIDER Neuro MRI patient cohort, scanned at diagnosis of tumour recurrence. For both patient cohorts, two sets of scans were taken 2-6 days apart. Each of these patient cohorts was analysed independently to determine if findings were consistent across different acquisition parameters. Parametric maps of Apparent Diffusion Coefficient (ADC) and Cerebral Blood Volume (CBV) were obtained from DWI and DCE data, respectively. Intensity normalisation and noise filtering were applied to the parametric maps in multiple permutations to give 7 distinct standardisation methods. Shape, first order and second order radiomic features for the parametric maps were calculated within the Gross Tumour Volume (GTV). The Intraclass Correlation Coefficient (ICC) was calculated between the feature value at each imaging timepoint. The ICC of first and second order features derived from images with each standardisation method was compared to the ICC of corresponding features derived from images without standardisation. Based on the average ICC of features derived from ADC images without image standardisation, first order features were the most repeatable in both patient cohorts. For ADC derived features in the QIN cohort, shape features were the second most repeatable followed by second order features. For ADC derived features in the RIDER cohort, second order features were the second most repeatable followed by shape features. In CBV images, shape features were the most repeatable followed by second order and then first order in both patient cohorts. No image standardisation method implemented in this study was found to significantly increase the repeatability of ADC-derived first or second order features. For first order CBV features z-score normalisation without noise filtering produced a significant improvement in feature repeatability in both patient cohorts. Radiomic feature repeatability is impacted by feature class. Image standardisation methods implemented in this study were not found to be effective at improving the repeatability of ADC-derived features and had limited utility for improving CBV derived features. Future radiomic studies should consider feature repeatability as an important factor in feature selection.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114781","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":"In Memoriam: Lyn Douglas Oliver AM MSc PhD (1941-2023).","authors":"Jeremy Booth, Clive Baldock","doi":"10.1007/s13246-025-01643-w","DOIUrl":"https://doi.org/10.1007/s13246-025-01643-w","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114794","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":"Comparative analysis of eMC algorithm dose calculations using GATE validation: impact of tissue heterogeneity on electron beam dosimetry.","authors":"Mohammed Rezzoug, Mustapha Zerfaoui, Yassine Oulhouq, Abdeslem Rrhioua, Omar Hamzaoui, Dikra Bakari","doi":"10.1007/s13246-025-01641-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01641-y","url":null,"abstract":"<p><strong>Purpose: </strong>Electron beam radiotherapy is a crucial modality for treating superficial tumors. Accurate dose calculation is essential for treatment efficacy and minimizing side effects. While Monte Carlo (MC) simulations are considered the gold standard for dose calculation, their computational cost can be prohibitive. The electron Monte Carlo (eMC) algorithm offers a faster alternative, but its accuracy, especially in heterogeneous environments, remains a concern.</p><p><strong>Methods and materials: </strong>This study compares electron beam dose distributions calculated using the eMC algorithm in a treatment planning system (TPS) with those obtained from full MC simulations using the GATE platform. We evaluated the eMC algorithm's performance across various electron energies (6, 9, and 12 MeV) and field sizes (6 × 6 cm² to 20 × 20 cm²), in both homogeneous water phantoms and heterogeneous phantoms incorporating lung-equivalent and bone-equivalent materials.</p><p><strong>Results: </strong>Results in homogeneous phantoms demonstrated generally good agreement between eMC and GATE, with some discrepancies observed in penumbra regions and at higher energies, particularly for larger field sizes. In heterogeneous phantoms, significant deviations were observed, particularly in lateral dose profiles near density interfaces and at higher beam energies, with percentage of points with less than 3% difference dropping considerably.</p><p><strong>Conclusion: </strong>These findings highlight the limitations of the eMC algorithm in accurately modeling complex tissue heterogeneities. While eMC provides acceptable accuracy in relatively simple scenarios, its performance degrades significantly in clinically realistic heterogeneous environments, necessitating caution in treatment planning and highlighting the ongoing need for improved dose calculation algorithms.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082255","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":"Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers.","authors":"Fatima Al Zegair, Brigid Betz-Stablein, Monika Janda, H Peter Soyer, Shekhar S Chandra","doi":"10.1007/s13246-025-01636-9","DOIUrl":"https://doi.org/10.1007/s13246-025-01636-9","url":null,"abstract":"<p><p>A naevus is a benign melanocytic skin tumour made up of naevus cells, characterised by variations in size, shape, and colour. Understanding naevi is essential due to their significant role as markers for the risk of developing melanoma. This study focused on creating a visual representation called a manifold that illustrates the distribution of two types of naevi: suspicious and non-suspicious. The research aimed to classify real naevi using generative adversarial networks (GANs), while also generating realistic synthetic samples and interpreting their distribution through a variational manifold. This inquiry holds promise for applying data-driven methods for early melanoma detection by identifying distinct features linked with suspicious naevi. Our variational autoencoder auxiliary classifier generative adversarial network (VAE-ACGAN) for suspicious naevi revealed a manifold with outstanding performance, including specificity, sensitivity, and area under the curve (AUC) scores, particularly representing suspicious naevi. These models surpassed various deep learning frameworks in key performance metrics while producing a manifold that indicated a significant distinction between the two categories in the resultant image, yielding high-quality and life-like representations of naevi. The results highlight the potential application of GANs in expanding data sets and enhancing the effectiveness of deep learning algorithms in dermatology. Accurate identification and categorisation of naevi could facilitate early melanoma detection and deepen our understanding of these skin lesions through an interpretable clustering method based on visual similarities.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076316","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}