AeSPa: Attention-guided Self-supervised Parallel Imaging for MRI Reconstruction

This study introduces a novel zero-shot scan-specific self-supervised reconstruction method for magnetic resonance imaging to reduce scan times. Conventional supervised reconstruction methods require large amounts of fully-sampled reference data, which is often impractical to obtain and can lead to artifacts by overly emphasizing learned patterns. Existing zero-shot scan-specific methods have attempted to overcome this data dependency but show limited performance due to insufficient utilization of k-space information and constraints derived from MRI forward model. To address these limitations, we introduce a framework utilizing all acquired k-space measurements for both network inputs and training targets. While this framework suffers from training instability, we resolve these challenges through three key components: an Attention-guided K-space Selective Mechanism (AKSM) that provides indirect constraints for non-sampled k-space points, Iteration-wise K-space Masking (IKM) that enhances training stability, and a robust sensitivity map estimation model utilizing cross-channel constraint that performs effectively even at high reduction factors. Experimental results on the FastMRI knee and brain datasets with reduction factors of 4 and 8 demonstrate that AeSPa achieves superior reconstruction quality and faster convergence compared to existing zero-shot scan-specific methods, making it suitable for practical clinical applications.

Experimental results on the FastMRI knee dataset using 1D Gaussian sampling with R = 8 with 16 ACS lines.

AeSPa Pipeline. The overview of the proposed method. The blue text denotes the update input for the coil-combined image estimation model, while the green text represents the update input for the sensitivity map estimation model.

Qualitative results for accelerated MRI reconstruction on the FastMRI brain data. Reconstruction was performed using 1D Gaussian random sampling with R = 4.

Qualitative results for accelerated MRI reconstruction on the FastMRI knee data. Reconstruction was performed using 1D Gaussian random sampling with R = 8.

Qualitative results of our model with varying reduction factors. The performance is demonstrated for 1D Gaussian random sampling with reduction factors ranging from R = 8 to 23.