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Related Experiment Video

Updated: May 12, 2026

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

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Published on: August 28, 2018

Deep learning-based post-hoc noise reduction improves quarter-radiation-dose coronary CT angiography.

Tomoro Morikawa¹, Tatsuya Nishii², Yuki Tanabe¹

¹Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan.

European Journal of Radiology

|June 12, 2025

Summary

Deep learning-based noise reduction significantly enhances quarter-dose coronary CT angiography (CCTA) image quality. This deep learning-based noise reduction improves diagnostic accuracy for coronary artery disease assessment.

Keywords:

CT angiography Computer Assisted Image Processing Coronary artery disease Deep learning

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Published on: February 21, 2025

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Coronary CT angiography (CCTA) is crucial for diagnosing coronary artery disease.
Reducing radiation dose in CCTA is a significant clinical goal.
Deep learning-based noise reduction (DLNR) offers potential for dose reduction without compromising image quality.

Purpose of the Study:

To assess the impact of DLNR on image quality, CAD-RADS assessment, and diagnostic performance.
To compare quarter-dose CCTA with DLNR against full-dose CCTA.
To validate DLNR on external datasets for CCTA applications.

Main Methods:

Retrospective review of 221 patients undergoing electrocardiogram-gated CCTA.
Utilized dose modulation for quarter-dose and full-dose acquisitions.
Applied a residual dense network for denoising quarter-dose images.
Assessed image quality, CAD-RADS agreement, and diagnostic performance for stenosis detection.

Main Results:

DLNR reduced noise in quarter-dose CCTA from 37 HU to 18 HU (P < 0.001).
DLNR improved CAD-RADS agreement from moderate to excellent (0.82).
Denoised images showed superior AUC (0.97) for significant stenosis detection compared to original quarter-dose images (0.93, P = 0.032).

Conclusions:

DLNR significantly enhances image quality in quarter-dose CCTA.
DLNR improves CAD-RADS assessment and diagnostic performance for significant stenosis.
DLNR is a promising technique for reducing radiation dose in CCTA while maintaining diagnostic efficacy.