Novel Deep Learning Reconstruction to Augment Contrast Enhancement: Initial Evaluation.

Abstract

Objective: To assess image quality between single-energy CT (SECT) and dual-energy CT (DECT) scans compared with a novel deep learning (DL) reconstruction for SECT used to improve contrast enhancement.

Methods: The raw data from a prior prospective HIPAA-compliant study (March through August 2022) was used to create a novel reconstruction in patients with biopsy-proven colorectal adenocarcinoma and liver metastases. Patients underwent 120 kVp SECT and DECT (50 keV reconstruction) abdominal scans in the portal venous phase in the same breath hold. Two readers independently assessed the scans.

Results: The final study group was 13 men and 2 women with a mean age of 60 years ± 10, a mean height of 171 cm ± 8, a mean weight of 87 kg ± 23, and a mean body mass index of 30 kg/m2 ± 6. Liver, pancreas, spleen, psoas muscle, and aorta HUs were all significantly higher with the virtual DL reconstruction compared with the 120 kVp series, but significantly lower than the 50 keV series (P<0.05). Readers scored the DL reconstruction to have better contrast enhancement than the standard 120 kVp series and improved artifacts, noise texture, and resolution compared with the 50 keV series (P<0.05).

Conclusions: Contrast enhancement with the new reconstruction is superior compared with the standard 120 kVp series approaching that of 50 keV DECT, but with improved perception of artifacts, noise texture, and resolution.