用生成扩散模型解决新生儿重症监护室MRI扫描慢与运动伪影问题
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI
- 构建无需训练的生成模型，作为图像先验解决逆问题
- 在低信噪比小数据下实现加速重建、运动校正和超分辨率
- 适用于临床新生儿MRI，提升扫描效率与图像质量
我们提出首个面向新生儿重症监护室（NICU）MRI的无采集依赖扩散生成模型，用于解决多种逆问题以缩短扫描时间并增强运动鲁棒性。NICU中使用的低场强（低于1.5特斯拉）永磁体MRI虽可非侵入式评估早产儿脑部异常，但面临扫描时间长、运动伪影严重的问题，且训练数据量小、信噪比（SNR）低。本研究基于真实临床新生儿MRI数据集，结合新颖的信号处理与机器学习方法，在低信噪比与小样本条件下训练扩散概率生成模型。该模型在推理阶段作为统计图像先验，无需重训练即可解决多种逆问题。实验验证了其在三个实际应用中的有效性：加速重建、运动校正与超分辨率。
原文摘要 · Abstract (English)
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.
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