On The Role of K-Space Acquisition in MRI Reconstruction Domain-Generalization

TL;DR

This paper investigates how learned k-space sampling patterns in MRI can be optimized for better domain generalization, demonstrating improved cross-domain reconstruction and proposing a method to enhance robustness by simulating acquisition variability during training.

Contribution

It systematically evaluates the transferability of learned sampling patterns across domains and introduces a novel training approach that incorporates acquisition uncertainty to improve robustness.

Findings

Models with learned sampling patterns generalize better across domains.

Introducing acquisition uncertainty during training enhances domain robustness.

Learned k-space trajectories can serve as an active tool for domain generalization.

Abstract

Recent work has established learned k-space acquisition patterns as a promising direction for improving reconstruction quality in accelerated Magnetic Resonance Imaging (MRI). Despite encouraging results, most existing research focuses on acquisition patterns optimized for a single dataset or modality, with limited consideration of their transferability across imaging domains. In this work, we demonstrate that the benefits of learned k-space sampling can extend beyond the training domain, enabling superior reconstruction performance under domain shifts. Our study presents two main contributions. First, through systematic evaluation across datasets and acquisition paradigms, we show that models trained with learned sampling patterns exhibitimproved generalization under cross-domain settings. Second, we propose a novel method that enhances domain robustness by introducing acquisition uncertainty during training-stochastically perturbing k-space trajectories to simulate variability across scanners and imaging conditions. Our results highlight the importance of treating kspace trajectory design not merely as an acceleration mechanism, but as an active degree of freedom for improving domain generalization in MRI reconstruction.