Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
662-04-003 ISMRM Abstract

Deep Learning-Reconstructed Thin-Slice VIBE Enhances Biliary Delineation and Lesion Detection in Hepatobiliary Phase MRI

Primary: Body - Liver
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
662-04-003 · Artificial Intelligence-Based Image Reconstruction in the Body · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: Deep learning reconstruction Gadoxetic acid-enhanced MRI Volumetric interpolated breath-hold examination
Accepted
Haoran Dai 1, Jili Chen1,2, Xuhao Song1,2, Kai Liu, Caixia Fu3,4,5,6, Mengsu Zeng1
1Zhongshan Hospital, Shanghai, China
2Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
3Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, P.R. China, China
4Magnetic resonance, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
5MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
6MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen， China, China
Presenting Author: Haoran Dai

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. D.-M. Koh, A. Ba-Ssalamah, G. Brancatelli, G. Fananapazir, M.I. Fiel, S. Goshima, S.-H. Ju, N. Kartalis, M. Kudo, J.M. Lee, Consensus report from the 9 th International Forum for Liver Magnetic Resonance Imaging: applications of gadoxetic acid-enhanced imaging, European Radiology 31 (2021) 5615-5628.
2. S. Baleato-González, J.C. Vilanova, A. Luna, R. Menéndez de Llano, J.P. Laguna-Reyes, D.M. Machado-Pereira, A. Bermúdez-Naveira, I. Osorio-Vázquez, L. Alcalá-Mata, R. García-Figueiras, Current and advanced applications of gadoxetic acid–enhanced MRI in hepatobiliary disorders, Radiographics 43(4) (2023) e220087.
3. F. Alicia, E.J.A.W. François, L.M. Razvan, P.D.H. Martijn, J.d.H. Robbert, F. Shirin, J.S.V. Inge, B. Daniël, A.d.M. Robert, N.M.I. Jan, I.E. Joris, V. Joanne, C.D. Michail, M.v.D. Otto, G.J.T. Maarten, Lesions hyper- to isointense to surrounding liver in the hepatobiliary phase of gadoxetic acid-enhanced MRI, European Radiology (2024).
4. T. Ensar, Radiological Imaging in Liver Tumors: Diagnosis and Management Strategies, null (2024).
5. L. Neeraj, Invited Commentary on “Hyperintense Liver Masses at Hepatobiliary Phase Gadoxetic Acid–enhanced MRI”, Radiographics (2020).
6. J.G. Nam, J.M. Lee, S.M. Lee, H.-J. Kang, E.S. Lee, B.Y. Hur, J.H. Yoon, E. Kim, M. Doneva, High acceleration three-dimensional T1-weighted dual echo Dixon hepatobiliary phase imaging using compressed sensing-sensitivity encoding: comparison of image quality and solid lesion detectability with the standard T1-weighted sequence, Korean journal of radiology 20(3) (2019) 438-448.
7. J.H. Yoon, J.M. Lee, M.H. Yu, E.J. Kim, J.K. Han, B.I. Choi, High-resolution T1-weighted gradient echo imaging for liver MRI using parallel imaging at high-acceleration factors, Abdominal imaging 39 (2014) 711-721.
8. J.M. Brendel, J. Jacoby, R. Dehdab, J. Herrmann, S. Ursprung, S. Werner, S. Gassenmaier, D. Nickel, K. Nikolaou, S. Afat, H. Almansour, Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty, Diagn Interv Imaging 106(3) (2025) 85-92.
9. H. Almansour, S. Gassenmaier, D. Nickel, S. Kannengiesser, S. Afat, J. Weiss, R. Hoffmann, A.E. Othman, Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity, Invest Radiol 56(8) (2021) 509-516.
10. J.M. Brendel, J. Jacoby, R. Dehdab, S. Ursprung, V. Fritz, S. Werner, J. Herrmann, A.S. Brendlin, S. Gassenmaier, F. Schick, D. Nickel, K. Nikolaou, S. Afat, H. Almansour, Prospective Deployment of Deep Learning Reconstruction Facilitates Highly Accelerated Upper Abdominal MRI, Acad Radiol 31(12) (2024) 4965-4973.
11. H. Wei, J.H. Yoon, S.K. Jeon, J.W. Choi, J. Lee, J.H. Kim, M.D. Nickel, B. Song, T. Duan, J.M. Lee, Enhancing gadoxetic acid–enhanced liver MRI: a synergistic approach with deep learning CAIPIRINHA-VIBE and optimized fat suppression techniques, European Radiology 34(10) (2024) 6712-6725.
12. J.H. Kim, J.H. Yoon, S.W. Kim, Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma, Abdom Radiol (NY) 49 (2024).
13. Y. Takayama, K. Sato, S. Tanaka, R. Murayama, R. Jingu, K. Yoshimitsu, 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, Abdominal Radiology 49(10) (2024) 3450-3463.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/662-04-003