Ultralow dose coronary calcium scoring CT at reduced tube voltage and current by using deep learning image reconstruction.

Objective: To explore the potential of the deep learning reconstruction (DLR) for ultralow dose calcium scoring CT (CSCT) with simultaneously reduced tube voltage and current.

Methods: In this prospective study, seventy-five patients (group A) undergoing routine dose CSCT (120kVp/30mAs) were followed by a low dose (120kVp/20mAs) scan and another 81 (group B) were followed by an ultralow dose (80kVp/20mAs) scan. The hybrid iterative reconstruction was used for the routine dose data while the DLR for data of reduced dose. The calcium score and risk categorization were compared, where the correlation was evaluated using the intraclass correlation coefficient (ICC). The noise suppression performance of DLR was characterized by the contrast-to-noise ratio (CNR) between coronary arteries and pericoronary fat.

Results: The effective dose was 0.32 ± 0.03 vs. 0.48 ± 0.05 mSv for the two scans in group A and 0.09 ± 0.01 vs. 0.49 ± 0.05 mSv in group B. No significant difference was found on CACSs within either group (A: p = 0.10, ICC=0.99; B: p = 0.14, ICC=0.99), nor was it different on risk categorization (A: p = 0.32, ICC=0.99; B: p = 0.16, ICC=0.99). The DLR images exhibited higher CNR in both groups (A: p < 0.001; B: p = 0.001).

Conclusions: The DLR allowed reliable calcium scoring in not only low dose CSCT with reduced tube current but ultralow dose CSCT with simultaneously reduced tube voltage and current, showing feasibility to be adopted in routine applications.