Magnetic resonance (MR) compressed sensing reconstruction explores image sparsity to make MR acquisition faster while still reconstructing high quality images. Modern picture archiving and communication systems allow efficient access to previous scans acquired of the same subject. In this work, we propose to use previous scans to enhance the reconstruction of follow-up scans using a deep learning model. Our model is composed of a reconstruction network that outputs an initial MR reconstruction, which is used as input to an enhancement network along with a co-registered previous scan. Our enhancement network improved quantitative metrics on average by 15%.
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