AIMC Topic: Coronary Angiography

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High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.

European radiology
OBJECTIVES: To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26 kg/m,...

Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features.

BMC medical imaging
BACKGROUND: Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA)...

Automated segmentation of metal stent and bioresorbable vascular scaffold in intravascular optical coherence tomography images using deep learning architectures.

Physics in medicine and biology
Percutaneous coronary intervention (PCI) with stent placement is a treatment effective for coronary artery diseases. Intravascular optical coherence tomography (OCT) with high resolution is used clinically to visualize stent deployment and restenosis...

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review.

Circulation. Cardiovascular imaging
The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements...

Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images.

Computers in biology and medicine
UNLABELLED: The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challengi...

Deep Learning-Based Approach to Automatically Assess Coronary Distensibility Following Kawasaki Disease.

Pediatric cardiology
Kawasaki disease is an acute vasculitis affecting children, which can lead to coronary artery (CA) aneurysms. Optical coherence tomography (OCT) has identified CA wall damage in KD patients, but it is unclear if these findings correlate with any dist...

Calcium Scoring at Coronary CT Angiography Using Deep Learning.

Radiology
Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challengin...

Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography.

European radiology
OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilitie...

Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study.

Japanese journal of radiology
PURPOSE: The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the ...