Data Consistent Variational Networks for Zero-shot Self-supervised MR Reconstruction
摘要
Variational Networks are a common approach in deep learning-based accelerated MR reconstruction. Due to their architecture, they may however fail in enforcing data consistency.We propose an adjustment to the Variational Network, integrating an optimization block that ensures consistency with the measured kspace points. We show the superiority of the method for zero-shot self-supervised 3D reconstruction quantitatively on retrospectively undersampled knee-data, and qualitatively in prospectively undersampled MR angiography images.