Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.

OBJECTIVES

To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancreatic ductal adenocarcinoma (PDAC) using normal-resolution (NR) CT scanner.

METHODS

Fifty patients with PDAC who underwent multiphase pancreas CT on a 320-row NR scanner were retrospectively analyzed. Images were reconstructed using hybrid iterative reconstruction (HIR), normal-resolution deep-learning reconstruction (NR-DLR), and SR-DLR at a 0.5-mm slice thickness. The matrix size was 512 × 512 for HIR and NR-DLR, and 1024 × 1024 for SR-DLR. Image noise and contrast-to-noise ratio (CNR) of pancreas, superior mesenteric artery, portal vein, and PDAC were quantified. Noise power spectrum (NPS) in the liver and edge rise slope (ERS) at the pancreas, artery, and vein were used to quantify noise properties and edge sharpness. Subjective evaluations included rankings of image sharpness, noise magnitude, texture fineness, and delineation of PDAC, pancreas margin, pancreatic duct, peripancreatic vessels, and hepatic lesions (1 = worst; 3 = best among three image series). Overall diagnostic quality was rated on a 5-point scale (1 = undiagnostic, 5 = excellent).

RESULTS

SR-DLR showed significantly lower image noise and higher CNR than HIR and NR-DLR (all, p < 0.001). NPS analysis revealed no significant difference in average spatial frequency between SR-DLR and NR-DLR (p = 0.770), both being higher than HIR (both, p < 0.001). ERS values of all structures were highest with SR-DLR (p < 0.001). SR-DLR received the highest subjective scores for all criteria, with significant differences from HIR and NR-DLR (all, p < 0.001).

CONCLUSION

SR-DLR improved both subjective and objective image quality, enhancing the delineation of all structures relevant to PDAC assessment using NR CT scanner.