Deep learning image reconstruction for 50-keV virtual monoenergetic dual-energy CT of the thyroid: a prospective "dual-low" dose study.

Published in European radiology. Abstract: Dual-energy CT (DECT) at 50 keV increases iodine attenuation but exponentially amplifies image noise. The feasibility of using deep learning image reconstruction (DLIR) to counteract this noise under a "dual-low" (low-radiation and low-contrast medium) thyroid CT protocol remains underexplored. To investigate the performance of a dual-low DECT protocol combined with DLIR in contrast-enhanced thyroid CT compared with a standard-dose protocol using adaptive statistical iterative reconstruction-Veo (ASIR-V). In this prospective study (August-December 2025), patients were randomly assigned to a standard-dose group (120 kVp, 1.0 mL/kg iodine, ASIR-V 50%) or a dual-low dose group (DECT, 0.6 mL/kg iodine). Dual-low spectral data were reconstructed into 50-keV virtual monoenergetic images using ASIR-V 50%, low-strength DLIR (DLIR-L), and high-strength DLIR (DLIR-H). Objective metrics (CT attenuation, image noise, contrast-to-noise ratio, edge rise slope (ERS), noise power spectrum) and subjective 5-point Likert scores were compared using independent-samples t-tests, paired t-tests, Mann-Whitney U tests, and Wilcoxon signed-rank tests. Sixty-four patients (mean age, 48.6 years ± 12.2; 49 women) were evaluated (32 per group). The dual-low group achieved a 61% reduction in effective radiation dose (0.36 mSv ± 0.05 vs 0.93 mSv ± 0.22; p < 0.001) and a 20% reduction in iodine intake (11.7 g ± 1.8 vs 14.6 g ± 4.0; p < 0.001). Despite these reductions, DLIR-H demonstrated significantly lowe...