361-01-009 ISMRM Abstract

Feasibility of Deep-Learning Based Synthesis of Gadoxetic Acid–Enhanced Hepatobiliary Phase Liver MRI

Primary: Analysis Methods - Image Synthesis and Translation

Secondary: Body - Liver

361-01-009 · Advanced Quantitative Liver MRI: Radiomics, Elastography, and AI · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row B

Keywords: Synthetic Contrast-enhanced MRI Deep learning-based Liver DCE-MRI

Accepted

Jiaying Zhao ¹, Hero K Hussain², Jacob Richardson², Vikas Gulani², Yun Jiang^1,2

¹Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States of America

²Department of Radiology, University of Michigan, Ann Arbor, United States of America

Presenting Author: Jiaying Zhao

Synopsis

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References

1. Baleato-González, Sandra, et al. "Current and advanced applications of gadoxetic acid–enhanced MRI in hepatobiliary disorders." Radiographics 43.4 (2023): e220087. DOI: 10.1148/rg.220087 [doi]

2. Poetter-Lang, Sarah, et al. "Quantification of liver function using gadoxetic acid-enhanced MRI." Abdominal Radiology 45.11 (2020): 3532-3544. DOI: 10.1007/s00261-020-02779-x [doi]

3. Chernyak, Victoria, et al. "Liver imaging reporting and data system (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients." Radiology 289.3 (2018): 816-830. DOI: 10.1148/radiol.2018181494 [doi]

4. Ringe, Kristina I., et al. "Gadoxetate disodium–enhanced MRI of the liver: part 1, protocol optimization and lesion appearance in the noncirrhotic liver." American Journal of Roentgenology 195.1 (2010): 13-28. DOI: 10.2214/AJR.10.4392 [doi]

5. Lowekamp, B. C., Chen, D. T., Ibáñez, L., & Blezek, D. (2013). The Design of SimpleITK. Frontiers in Neuroinformatics, 7, 45.

6. Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." European conference on computer vision. Cham: Springer International Publishing, 2016.

7. Avesta A, Hossain S, Lin M, Aboian M, Krumholz HM, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering (Basel). 2023 Feb 1;10(2):181. DOI: 10.3390/bioengineering10020181 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/361-01-009