A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI

TL;DR

This paper introduces a novel diffusion generative model tailored for neonatal MRI in NICU settings, enabling robust, acquisition-agnostic solutions for accelerated imaging, motion correction, and super-resolution despite limited and noisy data.

Contribution

It presents the first acquisition-agnostic diffusion model for neonatal MRI that effectively handles low SNR and small datasets to solve multiple inverse imaging problems without retraining.

Findings

Effective in accelerated MRI reconstruction

Improves motion artifact correction

Enhances image resolution in neonatal MRI

Abstract

We present the first acquisition-agnostic diffusion generative model for Magnetic Resonance Imaging (MRI) in the neonatal intensive care unit (NICU) to solve a range of inverse problems for shortening scan time and improving motion robustness. In-NICU MRI scanners leverage permanent magnets at lower field-strengths (i.e., below 1.5 Tesla) for non-invasive assessment of potential brain abnormalities during the critical phase of early live development, but suffer from long scan times and motion artifacts. In this setting, training data sizes are small and intrinsically suffer from low signal-to-noise ratio (SNR). This work trains a diffusion probabilistic generative model using such a real-world training dataset of clinical neonatal MRI by applying several novel signal processing and machine learning methods to handle the low SNR and low quantity of data. The model is then used as a statistical image prior to solve various inverse problems at inference time without requiring any retraining. Experiments demonstrate the generative model's utility for three real-world applications of neonatal MRI: accelerated reconstruction, motion correction, and super-resolution.