Deep learning-based image reconstruction for the multi-arterial phase images: improvement of the image quality to assess the small hypervascular hepatic tumor on gadoxetic acid-enhanced liver MRI

Su Min Yun; Seung Baek Hong; Nam Kyung Lee; Suk Kim; Yea Hee Ji; Hyung Il Seo; Young Mok Park; Byeong Gwan Noh; Marcel Dominik Nickel

doi:10.1007/s00261-024-04236-5

Deep learning-based image reconstruction for the multi-arterial phase images: improvement of the image quality to assess the small hypervascular hepatic tumor on gadoxetic acid-enhanced liver MRI

Abdom Radiol (NY). 2024 Mar 21. doi: 10.1007/s00261-024-04236-5. Online ahead of print.

Authors

Su Min Yun¹, Seung Baek Hong^{2

3}, Nam Kyung Lee¹, Suk Kim¹, Yea Hee Ji¹, Hyung Il Seo⁴, Young Mok Park⁴, Byeong Gwan Noh⁴, Marcel Dominik Nickel⁵

Affiliations

¹ Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea.
² Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea. cinematiclife7@hanmail.net.
³ Department of Radiology and Research Institute of Radiology, Pusan National University Hospital, Pusan National University School of Medicine, 179 Gudeok-ro, Seo-gu, Busan, 49241, Korea. cinematiclife7@hanmail.net.
⁴ Department of Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea.
⁵ Siemens Healthcare GmbH, Erlangen, Germany.

PMID: 38512517
DOI: 10.1007/s00261-024-04236-5

Abstract

Purpose: To evaluated the impact of a deep learning (DL)-based image reconstruction on multi-arterial-phase magnetic resonance imaging (MA-MRI) for small hypervascular hepatic masses in patients who underwent gadoxetic acid-enhanced liver MRI.

Methods: We retrospectively enrolled 55 adult patients (aged ≥ 18 years) with small hepatic hypervascular mass (≤ 3 cm) between December 2022 and February 2023. All patients underwent MA-MRI, subsequently reconstructed with a DL-based application. Qualitative assessment with Linkert scale including motion artifact (MA), liver edge (LE), hepatic vessel clarity (HVC) and image quality (IQ) was performed. Quantitative image analysis including signal to noise ratio (SNR), contrast to noise ratio (CNR) and noise was performed.

Results: On both arterial phases (APs), all qualitative parameters were significantly improved after DL-based image reconstruction. (LE on 1st AP, 1.22 vs 1.61; LE on 2nd AP, 1.21 vs 1.65; HVC on 1st AP, 1.24 vs 1.39; HVC on 2nd AP, 1.24 vs 1.44; IQ on 1st AP, 1.17 vs 1.45; IQ on 2nd AP, 1.17 vs 1.47, all p values < 0.05). The SNR, CNR and noise were significantly improved after DL-based image reconstruction. (SNR on AP1, 279.08 vs 176.14; SNR on AP2, 334.34 vs 199.24; CNR on AP1, 106.09 vs 64.14; CNR on AP2, 129.66 vs 73.73; noise on AP1, 1.51 vs 2.33; noise on AP2, 1.45 vs 2.28, all p values < 0.05).

Conclusions: Gadoxetic acid-enhanced MA-MRI with DL-based image reconstruction improved the qualitative and quantitative parameters. Despite the short acquisition time, high-quality MA-MRI is now achievable.

Keywords: Deep-learning image reconstruction; Gadoxetic acid; Liver; Magnetic resonance imaging; Multi-arterial-phase magnetic resonance imaging.