Volume-rendered reconstruction, obtaining 3D visualization from original CT datasets, is increasingly used by physicians and medical educators in various clinical and educational scenarios. Cinematic rendering is a novel 3D rendering algorithm that simulates the propagation and interaction of light rays as they pass through the volumetric data, showing a more photorealistic representation of 3D images than achieved with standard volume rendering.
Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data.
Non-invasive cardiac imaging has rapidly evolved during the last decade due to advancements in CT based technologies. Coronary CT angiography has been shown to reliably assess coronary anatomy and detect high risk coronary artery disease. However, this technique is limited to anatomical assessment, thus non-invasive techniques for functional assessment of the heart are necessary. CT myocardial perfusion is a new CT based technique that provides functional assessment of the myocardium and allows for a comprehensive assessment of coronary artery disease with a single modality when combined with CTA. This review aims to discuss dynamic CT myocardial perfusion as a new technique in the assessment of CAD.
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