# Clinical feasibility test of 60 kVp double-low-dose coronary CT angiography with a deep learning reconstruction algorithm

**Authors:** Xi Wu, Manman Zhu, Yixuan Zou, Jialin Luo, Weiling He, Wenjie Sun, Hui Shi, Peng Liu, Feng Huang

PMC · DOI: 10.1186/s13244-026-02223-6 · 2026-02-10

## TL;DR

This study shows that a new low-dose CT scan for heart arteries using deep learning can safely reduce radiation and contrast use while keeping diagnostic accuracy.

## Contribution

A novel deep learning reconstruction algorithm enables double-low-dose coronary CT angiography with high diagnostic consistency.

## Key findings

- 60 kVp double-low-dose CCTA reduced radiation dose by 86.5% and contrast dose by 36.4%.
- DLR improved coronary stenosis assessment specificity, positive predictive value, and accuracy compared to HIR.
- LD-DLR showed high consistency in CT-FFR values with routine-dose CCTA across multiple vessel levels.

## Abstract

To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm.

Eighty-nine patients (44 females, 59.9 ± 13.2 years, 23.1 ± 3.3 kg/m2) with known or suspected coronary artery disease were prospectively enrolled. Each patient underwent the double-low-dose CCTA (60-kVp, 28 mL contrast at 2.5 mL/s) and was immediately followed by routine-dose CCTA (100-kVp, 44 mL contrast at 4.0 mL/s). Routine-dose data were reconstructed using hybrid iterative reconstruction (RD-HIR), and double-low-dose data were reconstructed using both HIR (LD-HIR) and DLR (LD-DLR). The consistency of both coronary stenosis assessments and CT-derived fractional flow reserve (CT-FFR) values between low-dose and routine-dose images was quantified using receiver operating characteristic analysis at various levels. Segment-level image quality scores (IQS), signal-noise-ratio (SNR), and contrast-noise-ratio (CNR) were compared among three groups.

Double-low-dose CCTA achieved a significant reduction in both radiation dose (0.60 ± 0.12 mSv vs 4.43 ± 1.42 mSv) and contrast volume compared to routine-dose CCTA. For the per-segment level, LD-DLR showed significantly higher specificity (0.99 vs 0.94), positive predictive value (0.91 vs 0.68), and accuracy (0.98 vs 0.94) for ≥ 50% coronary stenosis compared to LD-HIR. The area under the curve of LD-DLR was significantly higher than LD-HIR for ≥ 50% stenosis at per-segment (0.94 vs 0.92), per-vessel (0.92 vs 0.89), and per-patient (0.92 vs 0.85) levels; and for CT-FFR ≤ 0.80 at per-vessel (0.94 vs 0.74), LAD-vessel (0.94 vs 0.71), and LCX-vessel (0.99 vs 0.67) levels. The IQS, SNR, and CNR of LD-DLR were not inferior to those of RD-HIR in all segments.

The 60 kVp double-low-dose CCTA with DLR can significantly reduce radiation dose and simultaneously maintain the high consistency of coronary stenosis and CT-FFR assessments with routine-dose CCTA.

The 60 kVp double-low-dose CCTA protocol is feasible with a novel DLR algorithm without compromising the performance of coronary stenosis and CT-FFR assessments.

Is a 60 kVp double-low-dose CCTA protocol with a DLR algorithm feasible for routine clinical application?The 60 kVp CCTA protocol with the DLR algorithm reduced radiation dose by 86.5% and contrast dose by 36.4%.The 60 kVp CCTA with DLR achieved high consistency of coronary stenosis and CT-FFR values with the routine-dose 100 kVp CCTA.

Is a 60 kVp double-low-dose CCTA protocol with a DLR algorithm feasible for routine clinical application?

The 60 kVp CCTA protocol with the DLR algorithm reduced radiation dose by 86.5% and contrast dose by 36.4%.

The 60 kVp CCTA with DLR achieved high consistency of coronary stenosis and CT-FFR values with the routine-dose 100 kVp CCTA.

## Linked entities

- **Diseases:** coronary artery disease (MONDO:0005010)

## Full-text entities

- **Diseases:** coronary stenosis (MESH:D023921), coronary artery disease (MESH:D003324)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12891270/full.md

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Source: https://tomesphere.com/paper/PMC12891270