Utility of super-resolution deep-learning reconstruction for improving visibility of hepatocellular carcinoma at hepatic dynamic CT: comparison with hybrid iterative reconstruction and conventional deep-learning reconstruction.

Purpose

The aim of this study was to evaluate whether Precise IQ Engine (PIQE), a super-resolution deep-learning reconstruction designed to enhance spatial resolution while reducing image noise using a deep convolutional neural network, improves the visibility of hepatocellular carcinoma (HCC) compared with hybrid iterative reconstruction (Adaptive Iterative Dose Reduction with three-dimensional processing [AIDR 3D]) and conventional deep-learning reconstruction (Advanced Intelligent Clear-IQ Engine [AiCE]) in hepatic dynamic CT.

Methods

AND MATERIALS We evaluated 64 patients with HCC who had undergone hepatic dynamic CT. Hepatic arterial- and equilibrium phase (HAP and EP) scans were reconstructed with AIDR, AiCE, and PIQE. A radiologist measured the standard deviation of attenuation in the liver parenchyma as the image noise and calculated the contrast-to-noise ratio (CNR) = (ROIT - ROIL)/N in HAP and (ROIL - ROIT)/N in EP. ROIL is the mean attenuation of the liver parenchyma, ROIT the mean tumor attenuation, and N the noise. HCC visibility was assessed by 2 additional radiologists and graded with a focus on tumor margin delineation on a 5-point confidence scale.

Results

PIQE showed the lowest image noise, followed by AiCE and AIDR, in both HAP and EP (all p < 0.01). The CNR of HCCs was highest with PIQE, followed by AiCE and AIDR, in both HAP and EP (all p < 0.01). Qualitative visibility scores for HCCs were also highest with PIQE, followed by AiCE and AIDR, in both HAP and EP (all p < 0.01).

Conclusion

PIQE significantly improves HCC visibility compared with AIDR and AiCE in hepatic dynamic CT, along with reduced image noise and increased CNR.