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 2 × 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.