Abstract #1768

PEARL: Cascaded Self-supervised Cross-fusion Learning For Parallel MRI Acceleration

Qingyong Zhu¹, Bei Liu², Zhuo-Xu Cui¹, Chentao Cao³, Xiaomeng Yan², Yuanyuan Liu³, Jing Cheng³, Yihang Zhou¹, Yanjie Zhu³, Haifeng Wang³, Hongwu Zeng⁴, and Dong Liang¹

¹Research Center for Medical AI, SIAT, Chinese Academy of Sciences, Shenzhen, China, ²School of Mathematics, Northwest University, Xi'an, China, ³Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, China, ⁴Shenzhen Children’s Hospital, Shenzhen, China

Synopsis

Keywords: AI/ML Image Reconstruction, Image Reconstruction, Self-supervised AI

Motivation: Supervised deep learning (SDL) has limitations due to data dependency, and self-supervised frameworks like DIP struggle with noise and artifacts.

Goal(s): Introducing PEARL, a novel self-supervised accelerated parallel MRI approach.

Approach: PEARL leverages joint deep decoders coupling with cross-fusion schemes based on multi-parameter priors to achieve enhanced reconstruction.

Results: PEARL outperforms the existing methods, demonstrating notable improvement under highly accelerated acquisition.

Impact: This study emphasizes the significance and potential of self-supervised learning in addressing critical MRI challenges.

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