Radiation and contrast dose reduction in coronary CT angiography for slender patients with 70 kV tube voltage and deep learning image reconstruction.

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Abstract

To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction (DLIR) in coronary computed tomography angiography (CCTA) for slender patients with body-mass-index (BMI) ≤25 kg/m2. Sixty patients for CCTA were randomly divided into 2 groups: group A with 120 kV and contrast agent dose of 0.8 mL/kg, and group B with 70 kV and contrast agent dose of 0.5 mL/kg. Group A used adaptive statistical iterative reconstruction-V (ASIR-V) with 50% strength level (50%ASIR-V) while group B used 50% ASIR-V, DLIR of low level (DLIR-L), DLIR of medium level (DLIR-M), and DLIR of high level (DLIR-H) for image reconstruction. The CT values and SD values of coronary arteries and pericardial fat were measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively evaluated by 2 radiologists using a five-point scoring system. The effective radiation dose (ED) and contrast dose were calculated and compared. Group B significantly reduced radiation dose by 75.6% and contrast dose by 32.9% compared to group A. Group B exhibited higher CT values of coronary arteries than group A, and DLIR-L, DLIR-M, and DLIR-H in group B provided higher SNR values and CNR values and subjective scores, among which DLIR-H had the lowest noise and highest subjective scores. Using 70 kV combined with DLIR significantly reduces radiation and contrast dose while improving image quality in CCTA for slender patients with DLIR-H having the best effect on improving image quality. The 70 kV and DLIR-H may be used in CCTA for slender patients to significantly reduce radiation dose and contrast dose while improving image quality.

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