Influence of computed tomography slice thickness on deep learning-based, automatic coronary artery calcium scoring software performance
نویسندگان
چکیده
Suh Young Kim, Joo Suh, Hye-Jeong Lee, Hwiyoung Hyungi Seo, Hee Jun Park, Dong Hyun Yang
منابع مشابه
Computed tomography (CT) based calcium scoring in the diagnosis and prognostication of coronary artery disease (CAD)
nvented by Sir Godfrey Hounsfield in the 1970s, computed tomography (CT) has greatly strengthened the arsenal of imaging technologies that is available to physicians to detect, prognosticate, and treat disease. The fundamental principle underlying CT scanning is the exposure of biological tissues to rotating fine beam X-rays, followed by the detection and processing of the radiation that these ...
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Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classi...
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AIMS The purpose of this study was to assess the relationship between calcium scoring (CS) and multi-slice computed tomography coronary angiography (MSCTA) and to determine if MSCTA has an incremental prognostic value to CS. METHODS AND RESULTS In 432 patients (59% male, age 58 +/- 11 years) referred for cardiac evaluation owing to suspected coronary artery disease (CAD), CS and 64-slice MSCT...
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OBJECTIVE To analyse coronary stents with multislice spiral computed tomography (MSCT) in comparison with coronary angiography. PATIENTS AND METHODS 310 patients referred for conventional coronary angiography underwent MSCT on the next day (16 x 0.75 mm cross section, 420 ms rotation, 110 ml contrast agent intravenously at 4 ml/s). Two independent blinded reviewers analysed the MSCT. RESULT...
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ژورنال
عنوان ژورنال: Quantitative imaging in medicine and surgery
سال: 2023
ISSN: ['2223-4292', '2223-4306']
DOI: https://doi.org/10.21037/qims-22-835