Accelerating Stroke MRI With Diffusion Probabilistic Models Through Large‐Scale Pre‐Training and Target‐Specific Fine‐Tuning

Purpose To develop a data‐efficient strategy for accelerated MRI reconstruction with Diffusion Probabilistic Generative Models (DPMs) that enables faster scan times in clinical stroke MRI when only limited fully‐sampled data are available. Methods Our simple training strategy first pre‐trains a DPM on a large, diverse collection of publicly available fastMRI brain data and then fine‐tunes on a small target dataset using carefully selected learning rates and fine‐tuning durations. The approach is evaluated on controlled fastMRI experiments and on clinical stroke MRI data with a blinded clinical reader study. Results DPMs pre‐trained on 4000 non‐FLAIR subjects and fine‐tuned on FLAIR data from only 20 target subjects achieve reconstruction performance comparable to models trained with substantially more target‐domain FLAIR data across multiple acceleration factors. Moderate fine‐tuning with a reduced learning rate yields improved performance, while insufficient or excessive fine‐tuning degrades reconstruction quality. In a blinded reader study of 80 subjects at a single clinical site, images reconstructed from accelerated data using the proposed approach are rated comparably to standard‐of‐care on the image quality and structural delineation metrics defined in this work. Conclusion Large‐scale pre‐training combined with targeted fine‐tuning can enable DPM‐based MRI reconstruction for our data‐constrained, accelerated clinical stroke MRI application. In the single‐site settings evaluated here, the proposed approach reduces the need for large application‐specific datasets while maintaining clinically acceptable image quality, providing preliminary evidence for pre‐trained and fine‐tuned diffusion models as a strategy for accelerated MRI in targeted applications.