Enhanced Partial Fourier MRI With Zero-Shot Deep Untrained Priors

Abstract

We present a novel method for partial Fourier reconstruction using a zero-shot unsupervised deep learning approach. Specifically, this method enhances partial Fourier reconstruction by integrating the traditional phase constraint with recent advancements in zero-shot deep learning techniques. Recently, a zero-shot deep learning method based on an untrained generative prior has been proposed, demonstrating effectiveness in multi-echo/contrast reconstruction. This approach is based on the assumption that MR images can be nonlinearly represented through an untrained artificial neural network, allowing for simultaneous image reconstruction and prior learning without requiring any training data. In this study, we integrate this framework with the virtual conjugate coil (VCC) phase constraint to enable robust partial Fourier reconstruction. We evaluated the proposed method on the fastMRI dataset, the QALAS multi-contrast dataset, and a low-field dataset. Our experiment results confirm that the proposed method yields improved reconstruction quality compared to existing methods, with approximately $1.15 \times $ to $2.5\times $ reductions in NRMSE. The proposed technique enables robust partial Fourier reconstruction, proving valuable in numerous applications. Moreover, it adopts a zero-shot approach, eliminating the need for training data, which enhances its utility in scenarios where data collection is challenging.

Keywords

• Zero-shot learning
• partial Fourier
• accelerated MRI reconstruction
• untrained networks