Abstract #0882

Hepatobiliary Phase Synthesis Using Multi-Task Learning GAN: Application to Liver Fibrosis Classification

Rencheng Zheng¹, Nannan Shi², Yuxin Shi², Zidong Yang³, Xueqin Xia¹, Hing-Chiu Chang⁴, Weibo Chen⁵, Ying-Hua Chu⁶, Chengyan Wang⁷, and He Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Department of Radiology, Shanghai Public Health Clinical Center, Shanghai, China, ³USC Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States, ⁴Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, ⁵Philips Healthcare, Shanghai, China, ⁶Siemens Healthineers, Shanghai, China, ⁷Human Phenome Institute, Fudan University, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, Liver

Motivation: Hepatobiliary phase (HBP) has important clinical diagnostic value for liver diseases, but its long acquisition time can pose issues with scanning resources and patient cooperation.

Goal(s): Our goal was to design a generative model for HBP synthesis based on early phases in hepatobiliary-specific contrast-enhanced MRI.

Approach: We proposed a multi-task learning deep learning model and evaluated its performance on a multi-center dataset.

Results: The proposed model exhibited superior HBP synthesis performance compared to the classic Pix2Pix model. The synthetic HBP was comparable to the real HBP, and significantly outperformed early phases in subsequent liver fibrosis grading tasks.

Impact: The proposed approach has the potential to accurately synthesize HBP, which is expected to be extended to clinical practice for rapid acquisition of HBP in hepatobiliary-specific contrast-enhanced MRI, thereby significantly reducing scanning time and alleviating clinical stress.

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