Deep learning image reconstruction improves visualization of arterial phase hyperenhancement and washout appearance on dual-energy CT for hepatocellular carcinoma: a non-inferiority study.

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Abstract

This study aimed to evaluate the clinical value of deep learning image reconstruction (DLIR)-based dual-energy CT (DECT) in improving image quality for hepatocellular carcinoma (HCC). This single-center retrospective analysis of a prospective cohort included patients enrolled between June 2024 and July 2025. Virtual monoenergetic images (VMI) at 40, 50, 60, and 74-keV (120 kVp-like) were reconstructed using ASiR-V 50%, DLIR-H (high), and DLIR-M (medium). All combinations of energy levels and reconstruction algorithms were compared using both quantitative metrics standard deviation (SD) of liver and lesion attenuation, signal-to-noise ratio (SNR), and lesion-to-liver contrast ratio (LLR) and semi-quantitative 5-point scores (overall noise, lesion edge sharpness, and conspicuity). The optimal reconstruction combination-derived DECT image was identified and compared with MRI for major HCC features of LI-RADS 2018, including arterial phase hyperenhancement (APHE) and nonperipheral washout appearance. Each patient yielded 36 image sets across three phases from various combinations of energy levels and algorithms. Quantitative analysis revealed DLIR-H/50-60-keV performed best across all objective metrics (all p < 0.05); with quantitative assessment, DLIR-H/50-keV was determined as the optimal protocol, which showed non-inferiority to MRI for detecting the two major HCC features of LI-RADS 2018. DLIR significantly enhances low-energy VMI quality and the visualization of major LI-RADS 2018 features in HCC. The DLIR-H/50-keV protocol demonstrates imaging performance approaching MRI standards, representing a promising reconstructive strategy for HCC assessment, particularly in clinical scenarios where MRI access is limited.

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