Diffusion probabilistic generative models for accelerated, in-NICU permanent magnet neonatal MRI.

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 r

SUBMITTER: Arefeen Y 

PROVIDER: S-EPMC12309888 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature