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

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2024年11月14日

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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

摘要

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.