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Coronary Angiography

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Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially ob...

Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses.

Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnos...

Segmenting 3D geometry of left coronary artery from coronary CT angiography using deep learning for hemodynamic evaluation.

Biomedical physics & engineering express
While coronary CT angiography (CCTA) is crucial for detecting several coronary artery diseases, it fails to provide essential hemodynamic parameters for early detection and treatment. These parameters can be easily obtained by performing computationa...

Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD.

International journal of cardiology
BACKGROUND: The predictive role of chest radiographs in patients with suspected coronary artery disease (CAD) is underestimated and may benefit from artificial intelligence (AI) applications.

[Future of interventional cardiology : Does everything revolve around AI and robotics?].

Herz
In recent years, software-assisted imaging systems, such as computed tomography, have contributed to the improvement of noninvasive options for the diagnostics of coronary heart disease (CHD). In addition, the possibilities of individual morphologica...

Motion artefact reduction in coronary CT angiography images with a deep learning method.

BMC medical imaging
BACKGROUND: The aim of this study was to investigate the ability of a pixel-to-pixel generative adversarial network (GAN) to remove motion artefacts in coronary CT angiography (CCTA) images.

Deep Learning Segmentation and Reconstruction for CT of Chronic Total Coronary Occlusion.

Radiology
Background CT imaging of chronic total occlusion (CTO) is useful in guiding revascularization, but manual reconstruction and quantification are time consuming. Purpose To develop and validate a deep learning (DL) model for automated CTO reconstructio...

Deep learning-based prediction of coronary artery stenosis resistance.

American journal of physiology. Heart and circulatory physiology
Coronary artery stenosis resistance (SR) is a key factor for noninvasive calculations of fractional flow reserve derived from coronary CT angiography (FFR). Existing computational fluid dynamics (CFD) methods, including three-dimensional (3-D) comput...

Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom.

PloS one
BACKGROUND: The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimick...