Data-driven Cartesian sampling design for Compressed Sensing MRI

We propose a novel, data-driven method for optimizing Cartesian undersampling patterns for Compressed Sensing. The method iteratively adds sampling points based on CS reconstructions of a training set. The performance of the proposed optimized sampling patterns are evaluated against the commonly used Variable Density undersampling methods. Our method shows improvements in both the Normalized Root Mean Square Error and the mean Structural Similarity index. The method generalizes to any reconstruction method that allows Cartesian undersampling in any number of dimensions and would enable optimization of patterns for a combination of CS and parallel imaging.

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