Ultra-Low-Dose Liver CT With Artificial Intelligence Iterative Reconstruction

Objective: To investigate the potential feasibility of ultra-low-dose (ULD) liver CT with the artificial intelligence iterative reconstruction (AIIR). Methods: Sixty-five patients who underwent triphasic contrast-enhanced liver CT were prospectively enrolled. Low tube voltage (80/100 kV) and tube current (35 to 78 mAs) were set in both portal venous phase (PVP) and delayed phase (DP). For each phase, an ULD acquisition (1.11 to 2.50 mGy) was taken followed immediately by a routine-dose (RD) acquisition (11.71 to 19.73 mGy). RD images were reconstructed with a hybrid iterative reconstruction algorithm (RD-HIR), while ULD images were reconstructed with both HIR (ULD-HIR) and AIIR (ULD-AIIR). The noise power spectrum (NPS) noise magnitude, average NPS spatial frequency, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for the quantitative assessment. Qualitative assessment was performed by 2 radiologists who independently scored the images for diagnostic acceptance. In addition, the radiologists identified focal lesions and characterized noncystic lesions as benign or malignant with both RD and ULD liver CT. Results: Among the enrolled patients (mean age: 58.6±12.9 y, 35 men), 234 lesions with a mean size of 1.27±1.56 cm were identified. In both phases, ULD-AIIR showed comparable NPS noise magnitude with RD-HIR (all P >0.017), and lower NPS noise than ULD-HIR (all P <0.001). Average NPS spatial frequency, SNR, and CNR were highest with ULD-AIIR, followed by RD-HIR and ULD-HIR (all P <0.001). ULD-AIIR showed comparable diagnostic acceptance scores with RD-HIR, while ULD-HIR failed to meet the diagnostic acceptance requirements. RD-HIR and ULD-AIIR achieved comparable detection rate (99.6% vs. 99.1%) and area under curve (AUC) of the receiver operating characteristic curve (ROC) in classifying benign (n=46) and malignant (n=58) noncystic lesions (0.98 vs. 0.97, P =0.3). Conclusions: With AIIR, it is potentially feasible to achieve ULD liver CT (60% dose reduction) while preserving the image and diagnostic quality.