Deep Learning Reconstruction for 40-keV Virtual Monoenergetic CT of Colon Cancer: Evaluation of Image Quality and Edge Sharpness

Background: 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. Purpose: 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. Materials and Methods: In this retrospective study (May 2024–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). Contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and edge rise slope (ERS) were measured for tumors, feeding arteries, and regional lymph nodes. Two radiologists scored overall quality and boundary definition (5-point Likert scale). Data were compared using the Friedman test and post-hoc Bonferroni correction. Results: DLIR-H yielded the lowest image noise and highest CNR and SNR across all anatomical targets compared with DLIR-M and ASIR-V 50% (all P  < .001). For colon tumors, the CNR of DLIR-H (5.4 ± 2.2) was 82% higher than that of ASIR-V 50% (3.0 ± 1.1, P  < .001). Although ASIR-V 50% maintained a higher ERS than DLIR-H (108.0 ± 15.2 vs 101.4 ± 14.1 HU/mm, P  < .001), DLIR-H received the highest subjective scores for overall image quality and lesion boundary definition (median, 5.0 [IQR: 4.0–5.0] vs 3.0 [IQR: 2.0–3.0]; P  < .001). Conclusion: In 40-keV virtual monoenergetic CT of colon cancer, DLIR-H significantly improves 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, facilitating low-iodine spectral protocols.