ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning
Journal:
arXiv
Published Date:
Jan 8, 2025
Abstract
Compressed sensing MRI seeks to accelerate MRI acquisition processes by
sampling fewer k-space measurements and then reconstructing the missing data
algorithmically. The success of these approaches often relies on strong priors
or learned statistical models. While recent diffusion model-based priors have
shown great potential, previous methods typically ignore clinically available
metadata (e.g. patient demographics, imaging parameters, slice-specific
information). In practice, metadata contains meaningful cues about the anatomy
and acquisition protocol, suggesting it could further constrain the
reconstruction problem. In this work, we propose ContextMRI, a text-conditioned
diffusion model for MRI that integrates granular metadata into the
reconstruction process. We train a pixel-space diffusion model directly on
minimally processed, complex-valued MRI images. During inference, metadata is
converted into a structured text prompt and fed to the model via CLIP text
embeddings. By conditioning the prior on metadata, we unlock more accurate
reconstructions and show consistent gains across multiple datasets,
acceleration factors, and undersampling patterns. Our experiments demonstrate
that increasing the fidelity of metadata, ranging from slice location and
contrast to patient age, sex, and pathology, systematically boosts
reconstruction performance. This work highlights the untapped potential of
leveraging clinical context for inverse problems and opens a new direction for
metadata-driven MRI reconstruction.
