Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI

TL;DR

This paper introduces a diffusion probabilistic generative model pipeline to accelerate neonatal MRI scans in NICU settings, effectively handling low SNR and limited data, and producing clinically adequate images at higher acceleration rates.

Contribution

The study develops a novel training pipeline for diffusion models tailored to low-quality neonatal MRI data, enabling faster scans without re-training for different acceleration factors.

Findings

Improved reconstruction quality with combined data and denoising pre-training

Model functions effectively at two acceleration rates without re-training

Clinically acceptable images reconstructed from 1.5x under-sampled data

Abstract

Purpose: Magnetic Resonance Imaging (MRI) enables non-invasive assessment of brain abnormalities during early life development. Permanent magnet scanners operating in the neonatal intensive care unit (NICU) facilitate MRI of sick infants, but have long scan times due to lower signal-to-noise ratios (SNR) and limited receive coils. This work accelerates in-NICU MRI with diffusion probabilistic generative models by developing a training pipeline accounting for these challenges. Methods: We establish a novel training dataset of clinical, 1 Tesla neonatal MR images in collaboration with Aspect Imaging and Sha'are Zedek Medical Center. We propose a pipeline to handle the low quantity and SNR of our real-world dataset (1) modifying existing network architectures to support varying resolutions; (2) training a single model on all data with learned class embedding vectors; (3) applying self-supervised denoising before training; and (4) reconstructing by averaging posterior samples. Retrospective under-sampling experiments, accounting for signal decay, evaluated each item of our proposed methodology. A clinical reader study with practicing pediatric neuroradiologists evaluated our proposed images reconstructed from 1.5x under-sampled data. Results: Combining all data, denoising pre-training, and averaging posterior samples yields quantitative improvements in reconstruction. The generative model decouples the learned prior from the measurement model and functions at two acceleration rates without re-training. The reader study suggests that proposed images reconstructed from approximately 1.5x under-sampled data are adequate for clinical use. Conclusion: Diffusion probabilistic generative models applied with the proposed pipeline to handle challenging real-world datasets could reduce scan time of in-NICU neonatal MRI.