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

Atsushi Nakamoto; Hiromitsu Onishi; Takashi Ota; Toru Honda; Takahiro Tsuboyama; Hideyuki Fukui; Kengo Kiso; Shohei Matsumoto; Koki Kaketaka; Takumi Tanigaki; Kei Terashima; Yukihiro Enchi; Shuichi Kawabata; Shinya Nakasone; Mitsuaki Tatsumi; Noriyuki Tomiyama

doi:10.1007/s11604-024-01685-2

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

Jpn J Radiol. 2024 Nov 14. doi: 10.1007/s11604-024-01685-2. Online ahead of print.

Authors

Atsushi Nakamoto¹, Hiromitsu Onishi^{2

3}, Takashi Ota², Toru Honda², Takahiro Tsuboyama⁴, Hideyuki Fukui², Kengo Kiso², Shohei Matsumoto², Koki Kaketaka², Takumi Tanigaki², Kei Terashima², Yukihiro Enchi⁵, Shuichi Kawabata⁵, Shinya Nakasone⁵, Mitsuaki Tatsumi², Noriyuki Tomiyama²

Affiliations

¹ Department of Future Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. a-nakamoto@radiol.med.osaka-u.ac.jp.
² Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
³ Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, 1-7, Yamadaoka, Suita, Osaka, 565-0871, Japan.
⁴ Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
⁵ Division of Radiology, Department of Medical Technology, Osaka University Hospital, 2-15, Yamadaoka, Suita, Osaka, 565-0871, Japan.

PMID: 39538066
DOI: 10.1007/s11604-024-01685-2

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.

Keywords: Artificial intelligence; Computed tomography; Iterative reconstruction; Super-resolution deep learning reconstruction.