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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.

October 22, 2026pubmed logopapers

Authors

Kondo S,Nakamura Y,Fonseca D,Higaki T,Nakano S,Nishigaki M,Maeda S,Akagi M,Narita K,Mitani H,Fukumoto W,Tatsugami F,Awai K

Affiliations (5)

  • Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. [email protected].
  • Canon Medical Systems, Kawasaki, Japan.
  • Canon Inc., Ota-ku, Japan.
  • Department of Diagnostic Radiology, Hiroshima Prefectural Hospital, Hiroshima, Japan.

Abstract

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. 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) = (ROI<sub>T</sub> - ROI<sub>L</sub>)/N in HAP and (ROI<sub>L</sub> - ROI<sub>T</sub>)/N in EP. ROI<sub>L</sub> is the mean attenuation of the liver parenchyma, ROI<sub>T</sub> 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. 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). PIQE significantly improves HCC visibility compared with AIDR and AiCE in hepatic dynamic CT, along with reduced image noise and increased CNR.

Topics

Journal Article

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