Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures

Abstract

Abstract Purpose To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms. Materials and methods This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.5 mm thickness) were reconstructed using SR-DLR, DLR, and HIR. Objective image noise and contrast-to-noise ratio (CNR) for liver parenchyma relative to muscle were assessed. Two radiologists independently graded image quality using a 5-point rating scale for image noise, sharpness, artifact/blur, and overall image quality. They also graded the visibility of small vessels, main pancreatic duct, ureters, adrenal glands, and right adrenal vein on a 5-point scale. Results SR-DLR yielded significantly lower objective image noise and higher CNR than DLR and HIR ( P < .001). The visual scores of SR-DLR for image noise, sharpness, and overall image quality were significantly higher than those of DLR and HIR for both readers ( P < .001). Both readers scored significantly higher on SR-DLR than on HIR for visibility for all structures ( P < .01), and at least one reader scored significantly higher on SR-DLR than on DLR for visibility for all structures ( P < .05). Conclusion SR-DLR reduced image noise and improved image quality of thin-slice abdominal CT images compared to HIR and DLR. This technique is expected to enable further detailed evaluation of small structures.