Towards a Unified Theoretical Framework for Self-Supervised MRI Reconstruction

The demand for high-resolution, non-invasive imaging continues to drive innovation in magnetic resonance imaging (MRI), yet prolonged acquisition times hinder accessibility and real-time applications. While deep learning-based reconstruction methods have accelerated MRI, their predominant supervised paradigm depends on fully-sampled reference data that are challenging to acquire. Recently, self-supervised learning (SSL) approaches have emerged as promising alternatives, but most are empirically designed and fragmented. Therefore, we introduce UNITS (Unified Theory for Self-supervision), a general framework for self-supervised MRI reconstruction. UNITS unifies prior SSL strategies within a common formalism, enabling consistent interpretation and systematic benchmarking. We prove that SSL can achieve the same expected performance as supervised learning. Under this theoretical guarantee, we introduce sampling stochasticity and flexible data utilization, which improve network generalization under out-of-domain distributions and stabilize training. Together, these contributions establish UNITS as a theoretical foundation and a practical paradigm for interpretable, generalizable, and clinically applicable self-supervised MRI reconstruction.