Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?

TL;DR

This paper explores the use of natural-domain foundation models as image priors for accelerated cardiac MRI reconstruction, demonstrating their robustness and transferability across different anatomical datasets.

Contribution

It introduces an unrolled reconstruction framework incorporating pretrained visual encoders and compares their performance to domain-specific models, highlighting improved cross-domain robustness.

Findings

Foundation models are competitive with state-of-the-art in-distribution methods.

They exhibit enhanced robustness in cross-domain scenarios with limited data.

Natural-image-pretrained models learn highly transferable structural representations.

Abstract

The emergence of large-scale pretrained foundation models has transformed computer vision, enabling strong performance across diverse downstream tasks. However, their potential for physics-based inverse problems, such as accelerated cardiac MRI reconstruction, remains largely underexplored. In this work, we investigate whether natural-domain foundation models can serve as effective image priors for accelerated cardiac MRI reconstruction, and compare the performance obtained against domain-specific counterparts such as BiomedCLIP. We propose an unrolled reconstruction framework that incorporates pretrained, frozen visual encoders, such as CLIP, DINOv2, and BiomedCLIP, within each cascade to guide the reconstruction process. Through extensive experiments, we show that while task-specific state-of-the-art reconstruction models such as E2E-VarNet achieve superior performance in standard in-distribution settings, foundation-model-based approaches remain competitive. More importantly, in challenging cross-domain scenarios, where models are trained on cardiac MRI and evaluated on anatomically distinct knee and brain datasets--foundation models exhibit improved robustness, particularly under high acceleration factors and limited low-frequency sampling. We further observe that natural-image-pretrained models, such as CLIP, learn highly transferable structural representations, while domain-specific pretraining (BiomedCLIP) provides modest additional gains in more ill-posed regimes. Overall, our results suggest that pretrained foundation models offer a promising source of transferable priors, enabling improved robustness and generalization in accelerated MRI reconstruction.