Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstruction.

PURPOSE: While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays for 3D data. Deep learning-based reconstruction methods can alleviate this, but typically rely on complex, highly parametrized architectures. METHODS: As an alternative, we introduce a rotation-invariant weakly convex ridge regularizer (WCRR). The resulting variational reconstruction approach is benchmarked against state-of-the-art methods on retrospectively simulated data and out-of-distribution prospective GoLF-SPARKLING and CAIPIRINHA acquisitions. RESULTS: Our approach achieves competitive reconstruction quality while using orders of magnitude fewer parameters and retaining the interpretability of a sparsity-promoting variational prior. CONCLUSION: In summary, WCRR provides a compact and interpretable learned variational prior for 3D non-Cartesian MRI reconstruction.