Abstract #0713

Synthesized Gd-EOB-DTPA-enhanced hepatobiliary phase MR images via generative adversarial learning

Kaixuan Zhao^1,2,3, Yan Liu⁴, Zhigang Wu⁵, Yongzhou Xu⁵, Zaiyi Liu^2,3, and Guangyi Wang²

¹Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, GuangZhou, China, ²Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China, Guangzhou, China, ³Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, Guangzhou, China, ⁴Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China, Guangzhou, China, ⁵Philips Healthcare Guangzhou Ltd., Guangzhou, China

Synopsis

Keywords: Liver, Liver

Motivation: Gd-EOB-DTPA-enhanced hepatobiliary phase (HBP) imaging is clinical routine for liver lesion identification, and is usually empirically conducted at 20 minutes after bolus injection.

Goal(s): Our goal was to demonstrate the feasibility of optimizing clinical workflow by synthesizing Gd-EOB-DTPA-enhanced HBP images via machine learning.

Approach: Precontrast and early-enhanced T1WIs (5-min after bolus injection) acquired at 3 T were used to synthesize HBP images via a generative adversarial network in 490 subjects.

Results: Our preliminary results showed that synthesized HBP images are visually comparable to acquired HBP images with high SSIM(0.87±0.08) and PSNR(29.6±2.25).

Impact: Machine learning synthesized HBP images could provide comparable diagnostic information to acquired HBP images, suggesting that machine learning might be used to optimize clinical workflow and greatly shorten acquisition time for Gd-EOB-DTPA-enhanced MRI.

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