2024/10/28 by Yamin Arefeen, Arefeen, Yamin, Brett Levac +3 · 1 citation
Engineering · Medicine · Physics and Astronomy · #Advanced MRI Techniques and Applications #Atomic and Subatomic Physics Research #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Microwave Imaging and Scattering Analysis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.21602
openalex publication_date 2024/10/28 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28
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.