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Abstract #3978

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

Yamin Ishraq Arefeen1,2, Brett Levac1, and Jonathan Tamir1
1Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States, 2Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction, Lower-field, Neonatal, Clinical Translation

Motivation: Accelerating lower-field MRI in the neonatal-intensive-care-unit may reduce motion artifacts and increase accessibility to a wider range of patients. However, parallel imaging and machine learning reconstruction models trained on adult MRI do not apply in the neonatal setting.

Goal(s): This work accelerates single-channel neonatal MRI with diffusion-probabilistic-generative models trained from limited and noisy data collected on a permanent magnet system.

Approach: The proposed training method combines datasets from multiple contrasts and orientations with class embeddings and applies a self-supervised denoiser before training. Diffusion posterior sampling reconstructs images from under-sampled k-space.

Results: Our method enables 1.5x reduction in scan-time using a single-channel.

Impact: The improvement in acquisition speed of T1 and T2 weighted lower field neonatal MRI protocols using diffusion-probabilistic-generative models, trained with methods designed to handle the noisy, limited data, improves accessibility of MRI to patients in the neonatal-intensive-care-unit.

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Keywords