用于磁共振弹性成像中组织弹性重构的物理信息神经网络

Matthew Ragoza, Kayhan Batmanghelich
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引用次数: 0

摘要

磁共振弹性成像(MRE)是一种无创量化组织硬度(弹性)的医学成像模式,常用于诊断肝纤维化。构建组织弹性图需要解决一个涉及偏微分方程 (PDE) 的逆问题。目前解决逆问题的数值技术对噪声敏感,并且需要明确说明物理关系。在这项工作中,我们应用物理信息神经网络来解决组织弹性重建的逆问题。我们的方法不依赖于数值微分,可以扩展到从解剖图像中学习相关关联,同时尊重物理约束。我们在模拟数据和非酒精性脂肪肝(NAFLD)患者队列的活体数据上评估了我们的方法。与数值基线相比,我们的方法对噪声的鲁棒性更强,对真实数据的准确性更高,而且通过结合解剖信息,其性能得到了进一步提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography.

Magnetic resonance elastography (MRE) is a medical imaging modality that non-invasively quantifies tissue stiffness (elasticity) and is commonly used for diagnosing liver fibrosis. Constructing an elasticity map of tissue requires solving an inverse problem involving a partial differential equation (PDE). Current numerical techniques to solve the inverse problem are noise-sensitive and require explicit specification of physical relationships. In this work, we apply physics-informed neural networks to solve the inverse problem of tissue elasticity reconstruction. Our method does not rely on numerical differentiation and can be extended to learn relevant correlations from anatomical images while respecting physical constraints. We evaluate our approach on simulated data and in vivo data from a cohort of patients with non-alcoholic fatty liver disease (NAFLD). Compared to numerical baselines, our method is more robust to noise and more accurate on realistic data, and its performance is further enhanced by incorporating anatomical information.

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