-Original-

Virtual Monochromatic Imaging of Half-Iodine-Load, Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction in Patients with Renal Insufficiency: A Clinical Pilot Study

Shingo Harashima^1,†, Rika Fukui^1,†, Wakana Samejima^1,†, Yuta Hirose^1,†, Toshiya Kariyasu^1,*, Makiko Nishikawa^1,†, Hidenori Yamaguchi^1,2,† and Haruhiko Machida^1,*

¹Department of Radiology, Tokyo Women's Medical University Adachi Medical Center, Tokyo, Japan
²Department of Radiology, Nippon Medical School Tama Nagayama Hospital, Tokyo, Japan
^†contributed equally
^*co-correspondence

Background: We retrospectively examined image quality (IQ) of thin-slice virtual monochromatic imaging (VMI) of half-iodine-load, abdominopelvic, contrast-enhanced CT (CECT) by dual-energy CT (DECT) with deep learning image reconstruction (DLIR).
Methods: In 28 oncology patients with moderate-to-severe renal impairment undergoing half-iodine-load (300 mgI/kg) CECT by DECT during the nephrographic phase, we reconstructed VMI at 40-70 keV with a slice thickness of 0.625 mm using filtered back-projection (FBP), hybrid iterative reconstruction (HIR), and DLIR; measured contrast-noise ratio (CNR) of the liver, spleen, aorta, portal vein, and prostate/uterus; and determined the optimal keV to achieve the maximal CNR. At the optimal keV, two independent radiologists compared each organ's CNR and subjective IQ scores among FBP, HIR, and DLIR to subjectively grade image noise, contrast, sharpness, delineation of small structures, and overall IQ.
Results: CNR of each organ increased continuously from 70 to 40 keV using FBP, HIR, and DLIR. At 40 keV, CNR of the prostate/uterus was significantly higher with DLIR than with FBP; however, CNR was similar between FBP and HIR and between HIR and DLIR. The CNR of all other organs increased significantly from FBP to HIR to DLIR (P < 0.05). All IQ scores significantly improved from FBP to HIR to DLIR (P < 0.05) and were acceptable in all patients with DLIR only.
Conclusions: The combination of 40 keV and DLIR offers the maximal CNR and a subjectively acceptable IQ for thin-slice VMI of half-iodine-load CECT.

J Nippon Med Sch 2025; 92: 69-79

Keywords
deep learning image reconstruction, dual-energy CT, image quality, iodine load reduction, virtual monochromatic imaging

Correspondence to
Toshiya Kariyasu, MD, PhD, Department of Radiology, Tokyo Women's Medical University Adachi Medical Center, 4-33-1 Kohoku, Adachi-ku, Tokyo 123-8558, Japan
kariyasu.toshiya@twmu.ac.jp

Received, August 19, 2024
Accepted, October 30, 2024