A computationally efficient method of robustly estimating coil sensitivities, and reconstructing under-sampled images using a data-driven regularised SENSE formalism that is commensurate with online deployment for Cartesian k-space acquisitions is presented.
Abstract
Objective
Parallel imaging is ubiquitous in MRI, enabling higher spatial and/or temporal resolution. However, successful unfolding is contingent on robust and accurate estimation of relative coil sensitivities, which often involves computation times that preclude online deployment. We present a computationally efficient method of robustly estimating coil sensitivities, and reconstructing under-sampled images using a data-driven regularised SENSE formalism that is commensurate with online deployment for Cartesian k-space acquisitions.
Materials And Methods
The proposed image reconstruction method via Multiple Orthogonal Reference Sensitivity Encoding (MORSE) estimates multiple sensitivities per voxel to address issues, such as rapidly varying sensitivities, chemical shift artefact, or insufficient fields of view. It simultaneously provides a data-driven regularisation term for noise control providing inherent adaptability to diverse imaging contexts.
Results
MORSE has been successfully deployed in multiple neuroimaging studies at both 3T and 7T, including functional studies of autobiographical memory processing, visual and auditory perception, and quantitative MRI studies of neurodegenerative diseases including Huntington's, Alzheimer's and Parkinson's. Exemplar image reconstructions are presented and compared with GRAPPA, ENLIVE, ESPIRiT and LORAKS. We also showcase application of MORSE outside of the brain via application in liver and knee imaging. MORSE consistently produced high-quality, artefact-free images with reconstruction times feasible for online deployment.
Discussion
The proposed method of sensitivity estimation and unfolding regularisation is flexible and robust. It is made available to the community in open-source as a library of functions within the vendor-agnostic Gadgetron image reconstruction framework.
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