Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning

• URL: http://arxiv.org/abs/2011.13614v1
• Date: Fri, 27 Nov 2020 09:08:05 GMT
• Title: Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
• Authors: Kehan Qi, Yu Gong, Xinfeng Liu, Xin Liu, Hairong Zheng, Shanshan Wang
• Abstract summary: We develop a re-weighted multi-task deep learning method to learn prior knowledge from the existing big dataset. We then utilize them to assist simultaneous MR reconstruction and segmentation from the under-sampled k-space data. Results show that the proposed method possesses encouraging capabilities for simultaneous and accurate MR reconstruction and segmentation.
• Score: 14.62432715967572
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications. In this paper, 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 the under-sampled k-space data. The multi-task deep learning framework is equipped with two network sub-modules, which are integrated and trained by our designed iterative teacher forcing scheme (ITFS) under the dynamic re-weighted loss constraint (DRLC). The ITFS is designed to avoid error accumulation by injecting the fully-sampled data into the training process. The DRLC is proposed to dynamically balance the contributions from the reconstruction and segmentation sub-modules so as to co-prompt the multi-task accuracy. The proposed method has been evaluated on two open datasets and one in vivo in-house dataset and compared to six state-of-the-art methods. Results show that the proposed method possesses encouraging capabilities for simultaneous and accurate MR reconstruction and segmentation.

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