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Imaging Studies for Cardiovascular System V: CT

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

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Related Experiment Video

Updated: Jul 31, 2025

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference

Andreas A Giannopoulos¹, Lukas Keller¹, Daniel Sepulcri¹

¹Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland.

AJR. American Journal of Roentgenology

|May 3, 2023

Summary

This summary is machine-generated.

A new deep learning algorithm computes on-site fractional flow reserve from coronary CT angiography (FFR-CT) rapidly and accurately. This method shows excellent diagnostic performance for assessing coronary artery stenosis, improving clinical implementation.

Keywords:

FFR-CT coronary CTA deep learning fractional flow reserve invasive angiography validation study

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Published on: December 9, 2021

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Last Updated: Jul 31, 2025

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Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

Area of Science:

Cardiovascular imaging
Medical artificial intelligence
Computational fluid dynamics

Background:

Fractional flow reserve from coronary CT angiography (FFR-CT) assesses coronary lesion significance.
Clinical adoption of FFR-CT is limited by off-site processing and long turnaround times.

Purpose of the Study:

To evaluate the diagnostic performance of on-site FFR-CT using a deep learning algorithm.
To compare on-site FFR-CT results with invasive hemodynamic indexes.

Main Methods:

Retrospective study of 59 patients with coronary CTA and invasive angiography.
On-site FFR-CT computed using a deep learning-based 3D computational flow dynamics model.
Comparison of FFR-CT with invasive fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR).

Main Results:

FFR-CT demonstrated strong correlation (r=0.81) with invasive FFR.
High diagnostic performance for hemodynamically significant stenosis (AUC=0.975), with 95.9% accuracy.
Mean analysis time per patient was under 8 minutes.
Excellent intraobserver (ICC=0.85) and interobserver (ICC=0.94) agreement.

Conclusions:

A high-speed, on-site deep learning FFR-CT algorithm offers excellent diagnostic accuracy and reproducibility.
This technology has the potential to streamline FFR-CT implementation in routine clinical practice.