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

• Published in: European radiology Vol. 32; no. 5; pp. 2912 - 2920
• Main Authors: Li, Wanjiang, Diao, Kaiyue, Wen, Yuting, Shuai, Tao, You, Yongchun, Zhao, Jin, Liao, Kai, Lu, Chunyan, Yu, Jianqun, He, Yong, Li, Zhenlin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022; Springer Nature B.V
• Subjects: Algorithms; Angiography; Background noise; Body mass; Body mass index; Body size; Computed Tomography; Computed Tomography Angiography - methods; Contrast Media; Coronary Angiography - methods; Deep Learning; Diagnostic Radiology; Electric potential; High strength; Humans; Image contrast; Image processing; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Imaging; Internal Medicine; Interventional Radiology; Iterative methods; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Noise; Noise measurement; Noise reduction; Quality assessment; Radiation; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Statistical analysis; Statistics; Ultrasound; Voltage; Computed tomography angiography; Deep learning; Coronary vessel; Radiation dosage
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-08424-5

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/m 2 , 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/m 2 ) 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 .