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.Comment: To be appear in Computerized Medical Imaging and Graphic