SynPoC: a high-quality generative diffusion model for transforming ultra-low-field point-of-care MRI using high-field MRI representations

TL;DR

SynPoC is a new AI model that improves the quality of low-field MRI images to resemble high-field MRI, potentially increasing accessibility in medical imaging.

Contribution

Introduces SynPoC, a conditional adversarial diffusion model that enhances ultra-low-field MRI images using high-field MRI representations.

Findings

SynPoC improves anatomical clarity and structural alignment in ultra-low-field MRI images.

Quantitative analysis confirms enhanced image quality resembling high-field MRI.

Model shows promise for research but requires further validation for diagnostic use.

Abstract

Ultra-low-field (ULF) point-of-care (PoC) Magnetic Resonance Imaging (MRI) offers a promising pathway to improve accessibility in medical imaging due to its portability and lower cost. However, the diagnostic utility of ULF MRI is currently limited by lower image quality, particularly in signal-to-noise ratio, resolution, and contrast. To address this, we introduce SynPoC, a generative diffusion model designed to enhance ULF MRI by synthesizing high-field MRI-like images. SynPoC employs a conditional adversarial diffusion framework that leverages both noise and contrast-specific features to model inter-field representations. We evaluated SynPoC across a multi-site dataset of 180 participants, including both healthy individuals and patients with a variety of brain conditions. The enhanced images exhibited improved anatomical clarity and structural alignment with corresponding high-field MRI, as supported by quantitative and volumetric analyses. Our model demonstrates promise for image quality enhancement and research applications; however, as with other generative approaches, there is a non-zero risk of hallucinated or misleading features, particularly near low-SNR boundaries and fine structures. We therefore provide synchronized slice-by-slice comparison videos (3T, PoC, SynPoC) to aid reader inspection and emphasize that SynPoC is not intended for diagnostic decision-making without additional safeguards and validation. Further validation is warranted before diagnostic use.

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