Gating of X-ray fluoroscopy images is required for catheter reconstruction for registration of pre-procedural images with fluoroscopy for guidance and biophysical modelling. We propose a novel and clinically useful retrospective method for automatic image-based cardiac and respiratory motion gating. The technique is based on tracking and statistical analysis of the shape of the coronary sinus catheter. We applied our method on five mono-plane imaging sequences comprising a total of 322 frames from five different patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. We established systole, end-inspiration and end-expiration gating with success rates of 100%, 89.47% and 81.25% respectively.
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http://dx.doi.org/10.1109/EMBC.2012.6347108 | DOI Listing |
NPJ Digit Med
January 2025
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.
View Article and Find Full Text PDFRespir Res
January 2025
National Heart and Lung Institute, Imperial College London, London, UK.
Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.
Methods: We evaluated the potential of an automated ILD quantification system (icolung) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD.
J Med Internet Res
January 2025
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Background: To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed.
View Article and Find Full Text PDFACS Omega
January 2025
Nanotechnology, IoT and Applied Machine Learning Research Group, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh.
Nanoparticles embedded in polymer matrices play a critical role in enhancing the properties and functionalities of composite materials. Detecting and quantifying nanoparticles from optical images (fixed samples-in vitro imaging) is crucial for understanding their distribution, aggregation, and interactions, which can lead to advancements in nanotechnology, materials science, and biomedical research. In this article, we propose an ensembled deep learning approach for automatic nanoparticle detection and oligomerization quantification in a polymer matrix for optical images.
View Article and Find Full Text PDFSci Rep
January 2025
Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, 79106, Freiburg, Germany.
Acute stroke management is time-sensitive, making time data crucial for both research and quality management. However, these time data are often not reliably captured in routine clinical practice. In this proof-of-concept study we analysed image-based time data automatically captured in the DICOM format.
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