Model-Based Single-Shot EPI Reconstruction with Sparsity Regularization

Echo planar imaging (EPI) is widely used clinically for its speed, but is known to be sensitive to non-idealities like B0 field inhomogeneity, eddy currents, and gradient nonlinearity. Such non-idealities are not typically managed during image reconstruction, resulting in geometrically distorted images. Post-processing corrections (e.g., image-based interpolation) usually tend to degrade resolution. In this work, a comprehensive model-based reconstruction framework that prospectively and simultaneously accounts for non-idealities in accelerated single-shot EPI acquisitions is proposed. Sparsity regularization is also incorporated to mitigate noise amplification. The proposed algorithm is demonstrated on brain MRI data acquired on a compact 3T MRI system.