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Abstract #0683

Accelerating High b-Value DWI Acquisition Using a Convolutional Recurrent Neural Network

Zheng Zhong1, Kanghyun Ryu1, Jae Eun Song1, Janhavi Singhal2, Guangyu Dan3, Kaibao Sun3, Shreyas S. Vasanawala1, and Xiaohong Joe Zhou3,4
1Radiology, Stanford University, Stanford, CA, United States, 2Homestead High School, Cupertino, CA, United States, 3Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, 4Radiology, Neurosurgery and Bioengineering, University of Illinois at Chicago, Chicago, IL, United States


DWI can probe tissue microstructures in many disease processes over a broad range of b-values. In the scenario where severe geometric distortion presents, non-single-shot EPI techniques can be used, but introduce other issues such as lengthened acquisition times, which often requires undersampling in kspace. Deep learning has been demonstrated to achieve many-fold undersampling especially when highly redundant information is present. In this study, we have applied a novel convolutional recurrent neural network (CRNN) to reconstruct highly undersampled (up to six-fold) multi-b-value, multi-direction DWI dataset by exploiting the information redundancy in the multiple b-values and diffusion gradient directions.

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