Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.
Authors
Affiliations (7)
Affiliations (7)
- The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China.
- Jiangsu Provincial Engineering Research Center for Medical Imaging and Digital Medicine, Xuzhou, China.
- CT Imaging Research Center, GE HealthCare China, Shanghai, China.
- The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. [email protected].
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China. [email protected].
- Jiangsu Provincial Engineering Research Center for Medical Imaging and Digital Medicine, Xuzhou, China. [email protected].
Abstract
Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Deep learning image reconstruction (DLIR) may address this limitation, but its effect on anatomical edge preservation across multiple targets requires investigation. To evaluate the impact of DLIR on objective and subjective image quality of 40-keV VMIs in colon adenocarcinoma, with emphasis on the trade-off between noise reduction and edge definition. In this completed retrospective study (patient enrollment window: May 2024 to February 2025), 60 patients (mean age, 62.8 years ± 15.1; 34 men) with confirmed colon adenocarcinoma underwent dual-energy CT using a low-iodine protocol (1.0 mL/kg). Portal venous phase data were reconstructed at 40 keV using adaptive statistical iterative reconstruction-V (ASIR-V) 50%, medium-strength DLIR (DLIR-M), and high-strength DLIR (DLIR-H). Objective metrics, including contrast-to-noise ratio (CNR), edge rise distance (ERD), and edge rise slope (ERS), were measured. Two radiologists independently scored five qualitative categories, including regional lymph node visualization, using a 5-point Likert scale. DLIR-H yielded the lowest image noise and highest CNR across all anatomical targets compared with DLIR-M and ASIR-V 50% (all P < 0.001). For colon tumors, the CNR of DLIR-H (5.4 ± 2.2) was 80% higher than that of ASIR-V 50% (3.0 ± 1.1, P < 0.001). Although ASIR-V 50% maintained a higher ERS than DLIR-H (median, 108.3 vs. 101.5 HU/mm; P < 0.001), the ERD remained highly stable across all algorithms (median, 2.5 mm; pairwise P > 0.05). Subjectively, DLIR-H received the highest scores for overall image quality and regional lymph node visualization (median, 5.0 and 4.5, respectively, vs. 3.0 for both in ASIR-V 50%; all P < 0.001). In 40-keV virtual monoenergetic CT of colon cancer, DLIR-H significantly improves objective and subjective image quality for tumors, vessels, and lymph nodes. While a minor objective edge-smoothing effect exists, DLIR-H provides an optimal balance between robust noise suppression and anatomical clarity. Although these findings suggest the potential to facilitate low-iodine spectral protocols, future diagnostic accuracy trials are required to confirm their true clinical impact.