Accelerated Multi-Contrast Parallel Imaging Reconstruction with Implicit Neural Representation

Motivation: Multi-contrast MR scans provide rich information for clinical diagnosis and research studies. However, long scan time is a limitation. Goal(s): an implicit neural representation is proposed for accelerated multi-contrast parallel imaging reconstruction. The proposed scan-specific method obviates the need for fully sampled priors. Approach: The spatial and temporal feature maps of an initial reconstruction are implicitly represented into the weights of a prior network. It exploits the physics-based parallel imaging forward model of sparsely sampled measurements. Results: The proposed method outperforms the evaluated parallel imaging techniques at acceleration rates as high as R=16 in both reconstructed echo images and parameter mapping. Impact: The proposed scan-specific method reconstructs multi-contrast images by implicit representation of the feature maps learned from interim reconstructions and exploitation of parallel imaging forward model in the training stage. It outperforms evaluated parallel imaging techniques.