Recent works have demonstrated that deep learning (DL) based compressed sensing (CS) implementation can accelerate Magnetic Resonance (MR) Imaging by reconstructing MR images from sub-sampled k-space data. However, network architectures adopted in previous methods are all designed by handcraft. Neural Architecture Search (NAS) algorithms can automatically build neural network architectures which have outperformed human designed ones in several vision tasks. Inspired by this, here we proposed a novel and efficient network for the MR image reconstruction problem via NAS instead of manual attempts. Particularly, a specific cell structure, which was integrated into the model-driven MR reconstruction pipeline, was automatically searched from a flexible pre-defined operation search space in a differentiable manner. Experimental results show that our searched network can produce better reconstruction results compared to previous state-of-the-art methods in terms of PSNR and SSIM with 4-6 times fewer computation resources. Extensive experiments were conducted to analyze how hyper-parameters affect reconstruction performance and the searched structures. The generalizability of the searched architecture was also evaluated on different organ MR datasets. Our proposed method can reach a better trade-off between computation cost and reconstruction performance for MR reconstruction problem with good generalizability and offer insights to design neural networks for other medical image applications. The evaluation code will be available at https://github.com/yjump/NAS-for-CSMRI.

翻译：近期研究表明，基于深度学习（DL）的压缩感知（CS）实现可通过从欠采样k空间数据重建磁共振图像来加速磁共振成像。然而，先前方法中采用的网络架构均为人工设计。神经架构搜索（NAS）算法可自动构建神经网络架构，已在多项视觉任务中超越人工设计网络。受此启发，本文通过NAS而非手动尝试，提出了一种新颖高效的磁共振图像重建网络。具体而言，我们将一个特定的细胞结构集成至模型驱动的磁共振重建流程中，并通过可微分方式从灵活预定义操作搜索空间中自动搜索该结构。实验结果表明，与先前最先进方法相比，我们的搜索网络能以4-6倍更少的计算资源，在PSNR和SSIM指标上取得更优的重建结果。我们开展了大量实验分析超参数对重建性能及搜索结构的影响，并在不同器官的磁共振数据集上评估了搜索架构的泛化能力。所提方法能针对磁共振重建问题实现计算成本与重建性能的更好权衡，且具备良好的泛化能力，可为其他医学图像应用的神经网络设计提供启示。评估代码将发布于https://github.com/yjump/NAS-for-CSMRI。