Robust Partial Fourier Reconstruction with Zero-shot Deep Untrained Generative Prior

Motivation: We introduce a novel partial Fourier reconstruction method. Goal(s): The objective is to enhance partial Fourier reconstruction by integrating the traditional phase constraint with the recent zero-shot deep learning approach. Approach: The proposed method combines the virtual conjugate coils (VCC) phase constraint with zero-shot deep untrained generative prior (ZS-DUGP), assuming MRI can be nonlinearly represented by untrained networks, enabling simultaneous image reconstruction and prior learning without external training data. This approach enables robust partial Fourier reconstruction. Results: Evaluation across diverse datasets, including the fastMRI, the QALAS multi-echo data, and the low-field MR data, validates its enhanced performance compared to existing techniques. Impact: We propose a novel partial Fourier reconstruction combining virtual conjugate coils with a zero-shot untrained generative network prior. It provides robust reconstruction without external training dataset, evaluated across various scenarios (parallel imaging, multi-echo/contrast imaging, low-field MR) demonstrating its utility.