Keywords: Quantitative Imaging, Image Reconstruction
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.
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