Enhanced MRI Reconstruction Network using Neural Architecture Search

• URL: http://arxiv.org/abs/2008.08248v1
• Date: Wed, 19 Aug 2020 03:44:31 GMT
• Title: Enhanced MRI Reconstruction Network using Neural Architecture Search
• Authors: Qiaoying Huang, Dong Yang, Yikun Xian, Pengxiang Wu, Jingru Yi, Hui Qu, Dimitris Metaxas
• Abstract summary: We present an enhanced MRI reconstruction network using a residual in residual basic block. For each cell in the basic block, we use the differentiable neural architecture search (NAS) technique to automatically choose the optimal operation. This new heterogeneous network is evaluated on two publicly available datasets and outperforms all current state-of-the-art methods.
• Score: 22.735244777008422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures. The cascaded network architecture for MRI reconstruction has been widely used, while it suffers from the "vanishing gradient" problem when the network becomes deep. In addition, homogeneous architecture degrades the representation capacity of the network. In this work, we present an enhanced MRI reconstruction network using a residual in residual basic block. For each cell in the basic block, we use the differentiable neural architecture search (NAS) technique to automatically choose the optimal operation among eight variants of the dense block. This new heterogeneous network is evaluated on two publicly available datasets and outperforms all current state-of-the-art methods, which demonstrates the effectiveness of our proposed method.

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