Self-Consistency-Driven Test-Time Prompt Tuning for All-in-One MR Reconstruction Model

Motivation: Recently, all-in-one image restoration models based on prompt learning have been proposed. However, MRI is influenced by numerous factors, introducing additional complexity that challenges the development of an all-in-one model specifically MRI. Goal(s): Develop an all-in-one model tailored for MRI. Approach: It remains challenging to build an MRI model generalized across scan location, sequence parameters, sampling methods through prompt learning. Inspired by scan-specific models, we incorporate test-time tuning of prompts (TTTP) using MRI physics-informed priors, enabling scan-specific adjustments to achieve robust generalization across various scenarios. Results: Through TTTP, an all-in-one MRI model is constructed, effectively generalizing across scan regions, sequence parameters, sampling methods. Impact: This model can adapt to all MRI scenarios, facilitating simplified installation and maintenance.