Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging

Yukihisa Takayama; Keisuke Sato; Shinji Tanaka; Ryo Murayama; Ryotaro Jingu; Kengo Yoshimitsu

doi:10.1007/s00261-024-04374-w

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging

Abdom Radiol (NY). 2024 May 16. doi: 10.1007/s00261-024-04374-w. Online ahead of print.

Authors

Yukihisa Takayama¹, Keisuke Sato², Shinji Tanaka², Ryo Murayama², Ryotaro Jingu³, Kengo Yoshimitsu²

Affiliations

¹ Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan. ytakayama@fukuoka-u.ac.jp.
² Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
³ Radiology Center, Fukuoka University Hospital, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.

PMID: 38755452
DOI: 10.1007/s00261-024-04374-w

Abstract

Purpose: To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI).

Materials and methods: This retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test.

Results: BH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05).

Conclusion: The use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

Keywords: Deep learning-based reconstruction (DLR); Gd-EOB-MRI; Hepatobiliary phase; Liver tumor; Magnetic resonance imaging (MRI).