Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning

Abstract

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 samples are available. Methods: Our simple training strategy, inspired by the foundation model paradigm, first trains a DPM on a large, diverse collection of publicly available brain MRI data in fastMRI and then fine-tunes on a small dataset from the target application 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 approximately 4000 subjects with non-FLAIR contrasts 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. Experiments reveal that moderate fine-tuning with a reduced learning rate yields improved performance, while insufficient or excessive fine-tuning degrades reconstruction quality. When applied to clinical stroke MRI, a blinded reader study involving two neuroradiologists indicates that images reconstructed using the proposed approach from $2 \times$ accelerated data are non-inferior to standard-of-care in terms of image quality and structural delineation. Conclusion: Large-scale pre-training combined with targeted fine-tuning enables DPM-based MRI reconstruction in data-constrained, accelerated clinical stroke MRI. The proposed approach substantially reduces the need for large application-specific datasets while maintaining clinically acceptable image quality, supporting the use of foundation-inspired diffusion models for accelerated MRI in targeted applications.

Figures (10)

• Figure 1: (A) The standard DPM training pipeline trains exclusively on the target-domain dataset, but if the target dataset is small, this can lead to degraded performance. (B) The proposed training pipeline mitigates data scarcity by first pre-training the DPM on a large, diverse external MRI dataset, followed by fine-tuning on a small target-domain dataset using a reduced learning rate and limited number of epochs. The fine-tuned model is then used for posterior sampling–based reconstruction from under-sampled k-space.
• Figure 2: Quantitative results from the controlled fastMRI experiments where DPMs were fine-tuned on 20 subjects to reconstruct fastMRI flair data. NRMSE on 500 validation slices as a function of fine-tuning duration (epochs) is plotted for different learning rates and acceleration factors. Across all acceleration factors, moderate fine-tuning improves reconstruction quality relative to no fine-tuning, while excessive fine-tuning leads to degraded performance, particularly at higher learning rates.
• Figure 3: A representative example from the fastMRI FLAIR experiment. (A) Quantitative NRMSE as a function of fine-tuning duration for different learning rates, highlighting that a comparatively smaller learning rate and fewer fine-tuning epochs yields the best performance. (B) Fully sampled reference FLAIR image. (C) Zoomed reconstructions obtained using different learning rates with 650 fine-tuning epochs, compared against the fully sampled reference. (D) Reconstructions obtained using a fixed learning rate of $1\times10^{-5}$ across different fine-tuning durations. Appropriate fine-tuning hyperparameters lead to visibly improved structural fidelity and reduced artifacts, as highlighted by the orange arrows.
• Figure 4: (A) DPMs are compared across methods with access to different amounts of target-domain FLAIR data. Methods 1–3 are trained using FLAIR data from all 344 subjects, while Methods 5 and 6 are trained using 20 FLAIR subjects and the external dataset. The proposed approach (Method 4), pre-trained on a large external dataset and fine-tuned on 20 FLAIR subjects, achieves comparable NRMSE to Methods 1-3, despite using substantially less target domain data and consistently outperforms Methods 5,6 trained on the same limited data. (B) displays example reconstructions from this experiment.
• Figure 5: Using the best fine-tuned DPM, average NRMSE on fastMRI FLAIR validation data is plotted as a function of the data-consistency step size $\zeta$ for multiple acceleration factors. Red markers denote the default choice $\zeta = 1$, while green markers indicate the value of $\zeta$ that minimizes reconstruction error at each acceleration. The optimal $\zeta$ increases with acceleration.