Adaptive prompting for dual-task learning: Towards high-quality MRI reconstruction from unidentified degradation

Degradation poses significant challenges to magnetic resonance imaging (MRI) scans. Unfortunately, degradation is inevitable and difficult to identify. Common sources of degradation arise from factors such as subject motion, thermal noise, and scan expedition. Recent reconstruction-based methods primarily address reconstructions for scans suffering from a single, well-defined degradation source. In practical scenarios, however, multiple and unidentified sources of degradation frequently arise simultaneously within a single scan. Existing solutions therefore rely on sequential pipelines that first identify degradation sources and then apply degradation-specific models, which may overlook degradation sources and compromise scan integrity through repeated reconstructions. This study targets single-stage reconstruction of MRI scans affected by a combination of various unidentified degradation sources. We proposed a unified reconstruction framework based on a dual-task learning strategy with prompt adaptation. Our technique focuses on learning effective degradation representations from degraded images, facilitating high-quality reconstruction of MRI scans with both high spatial resolution and elevated signal-to-noise ratio (SNR) while mitigating motion artifacts. We evaluated our method on three public datasets comprising clean and degraded MRI scans from 150 subjects, including unidentified degradations from five sources and real in-scanner motion artifacts. Experimental results demonstrated that our approach surpassed leading methods in terms of motion correction, SNR improvement, and resolution enhancement. The code is available at: https://github.com/NingJiang-git/UniRecon.