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

2024-10-28

AI Summary

To address clinical bottlenecks in low-field (<1.5 T) neonatal MRI in the NICU—including prolonged scan times, severe motion artifacts, scarce training data, and low signal-to-noise ratio (SNR)—this work introduces the first acquisition-agnostic diffusion generative model as a universal image prior. We pioneer the adaptation of diffusion models to real-world NICU neonatal MRI by proposing: (i) an adaptive noise scheduling scheme tailored for low-SNR and few-shot regimes; (ii) low-rank k-space modeling; (iii) physics-driven embedding of the forward operator; and (iv) unsupervised motion artifact modeling. Without fine-tuning, the model jointly solves three inverse problems—accelerated reconstruction, motion correction, and super-resolution—achieving 2–4× acceleration (PSNR gain +3.2 dB), marked suppression of head motion artifacts, and 2× super-resolution while preserving fine anatomical details—all with zero task-specific retraining on real clinical data.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web search models and ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Solving inverse problems for faster neonatal MRI scans
• Improving motion robustness in low-field NICU MRI
• Enhancing image quality from low-SNR, small neonatal datasets

Innovation

• Generative diffusion model for neonatal MRI
• Handles low SNR and small datasets
• Solves inverse problems without retraining