Improved Image Quality of Virtual Monochromatic Images with Deep Learning Image Reconstruction Algorithm on Dual-Energy CT in Patients with Pancreatic Ductal Adenocarcinoma

This study aimed to evaluate the image quality of virtual monochromatic images (VMIs) reconstructed with deep learning image reconstruction (DLIR) using dual-energy CT (DECT) to diagnose pancreatic ductal adenocarcinoma (PDAC). Fifty patients with histologically confirmed PDAC who underwent multiphasic contrast-enhanced DECT between 2019 and 2022 were retrospectively analyzed. VMIs at 40-100 keV were reconstructed using hybrid iterative reconstruction (ASiR-V 30% and ASiR-V 50%) and DLIR (TFI-M) algorithms. Quantitative analyses included contrast-to-noise ratios (CNR) of the major abdominal vessels, liver, pancreas, and the PDAC. Qualitative image quality assessments included image noise, soft-tissue sharpness, vessel contrast, and PDAC conspicuity. Noise power spectrum (NPS) analysis was performed to examine the variance and spatial frequency characteristics of image noise using a phantom. TFI-M significantly improved image quality compared to ASiR-V 30% and ASiR-V 50%, especially at lower keV levels. VMIs with TFI-M showed reduced image noise and higher pancreas-to-tumor CNR at 40 keV. Qualitative evaluations confirmed DLIR's superiority in noise reduction, tissue sharpness, and vessel conspicuity, with substantial interobserver agreement (κ = 0.61-0.78). NPS analysis demonstrated effective noise reduction across spatial frequencies. DLIR significantly improved the image quality of VMIs on DECT by reducing image noise and increasing CNR, particularly at lower keV levels. These improvements may improve PDAC detection and assessment, making it a valuable tool for pancreatic cancer imaging.