Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction. (arXiv:2002.09625v7 [eess.IV] UPDATED)

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 …

algorithms architecture architectures arxiv build data deep learning image images imaging implementation nas network neural architecture search search sensing space