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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

  • Computed Tomography
  • Published:
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Abstract

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/m2, in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V).

Methods

A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group (n = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group (n = 50) with 120 kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal–noise-ratio (SNR) for vessel) were compared between the two groups.

Results

Low-dose group used significantly reduced contrast dose (23.82 ± 3.69 mL, 50.6% reduction) and radiation dose (0.75 ± 0.14 mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38 mL and 1.65 ± 0.66 mSv, respectively) (all p < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p < 0.001) compared to the conventional group.

Conclusions

The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V.

Key Points

• The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol.

• CCTA of normal size (BMI < 26 kg/m2) patients acquired at sub-mSv radiation dose and 24 mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement.

• DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination.

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Abbreviations

ASIR-V:

Volume-based adaptive statistical iterative reconstruction

BMI:

Body mass index

CAD:

Coronary heart disease

CCS:

Chronic coronary syndrome

CCTA:

Coronary computed tomography angiography

CIN:

Contrast-induced nephropathy

CNR:

Contrast-noise-ratio

CPR:

Curved planar reformat

CTDIvol:

Volumetric CT dose index

DLIR:

Deep learning image reconstruction

DLP:

Dose-length product

DNN:

Deep neural networks

ED:

Effective dose

FBP:

Filtered back projection

HR:

Heart rate

IR:

Iterative reconstruction

LAD:

Left anterior descending branch

LCX:

Left circumflex

MI:

Myocardial infarction

MIP:

Maximum intensity projection

NI:

Noise index

RCA:

Right coronary artery

ROI:

Region of interest

SNR:

Signal–noise-ratio

SSF:

Snapshot freeze

VR:

Volume rendering

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Funding

The trial is funded by the Science and Technology Program of Sichuan (grant number: 2019YFS0522) and the 1.3.5 project for disciplines of excellence-Clinical Research Incubation Project, West China Hospital Sichuan University (grant number: ZYGD18019 and 2021HXFH021).

Author information

Author notes

  1. Wanjiang Li and Kaiyue Diao contributed equally to the work and should be considered as co-first authors.

Authors and Affiliations

  1. Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China

    Wanjiang Li, Kaiyue Diao, Yuting Wen, Tao Shuai, Yongchun You, Jin Zhao, Kai Liao, Chunyan Lu, Jianqun Yu & Zhenlin Li

  2. Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China

    Yong He

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  1. Wanjiang Li
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Corresponding authors

Correspondence to Yong He or Zhenlin Li.

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Guarantor

The scientific guarantor of this publication is Zhenlin Li and Yong He.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

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• prospective

• randomised controlled trial

• performed at one institution

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Yong He and Zhenlin Li co-supervised this work.

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Li, W., Diao, K., Wen, Y. et al. 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. Eur Radiol 32, 2912–2920 (2022). https://doi.org/10.1007/s00330-021-08424-5

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  • DOI: https://doi.org/10.1007/s00330-021-08424-5

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