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Abstract #2415

Multi-task MR imaging with deep learning

Kehan Qi1, Yu Gong1,2, Haoyun Liang1, Xin Liu1, Hairong Zheng1, and Shanshan Wang1
1Paul C Lauterbur Research Center, Shenzhen Inst. of Advanced Technology, shenzhen, China, 2Northeastern University, Shenyang, China

Noises, artifacts, and loss of information caused by the MR reconstruction may compromise the final performance of the downstream applications such as image segmentation. In this study, we develop a re-weighted multi-task deep learning method to learn prior knowledge from the existing big dataset and then utilize them to assist simultaneous MR reconstruction and segmentation from under-sampled k-space data. It integrates the reconstruction with segmentation and produces both promising reconstructed images and accurate segmentation results. This work shows a new way for the direct image analysis from k-space data with deep learning.

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