Data-driven reconstruction algorithms for Magnetic Resonance Elastography

磁共振弹性成像（MRE）是一种功能强大的诊断成像技术，可测量疾病引起的生物组织生物力学特性的变化。诺丁汉大学彼得·曼斯菲尔德爵士成像中心最近开始了MRE研究，在飞利浦3-特斯拉Ingenia磁共振成像（MRI）扫描仪上安装了一个MRE系统。MRE的工作原理是向组织传递机械波，用MRI测量，这些波的测量结果通过专门的重建算法转换成估计的生物力学特性。这些算法解决了一个相反的问题：从MR成像数据开始，它们估计组织的生物力学特性，从而允许区分健康和患病组织。当前的重建算法需要吸收大量的MRI成像数据噪声，如何准确识别病灶的位置和边界是当前重建算法面临的主要挑战。该项目的目标是利用最先进的贝叶斯反演技术开发和验证新的MRE数据重建算法。这些算法的目的是能够准确地重建组织属性，并量化估计/重建属性中的不确定性。这些算法将通过将MRI数据同化为一类异质和各向异性的生物力学模型，来显著改善基于MRI的诊断。该项目开发的算法的验证将使用彼得·曼斯菲尔德爵士成像中心获得的数据进行。

Magnetic Resonance Elastography (MRE) is a powerful diagnostic imaging technique that measures changes in the biomechanical properties of biological tissue caused by disease. MRE research has recently begun at the Sir Peter Mansfield Imaging Centre, University of Nottingham, with the installation of an MRE system on the Philips 3-Tesla Ingenia Magnetic Resonance Imaging (MRI) scanner. MRE works by delivering mechanical waves to the tissue, which are measured using MRI, and these wave measurements are converted into estimated biomechanical properties using specialised reconstruction algorithms. These algorithms solve an inverse problem: starting from MR imaging data, they estimate tissue biomechanical properties, thereby allowing the differentiation of healthy and diseased tissue. The accurate identification of the disease location and boundaries is a main challenge for current reconstruction algorithms, which are required to assimilate a large amount of noisy MRI imaging data. The objective of this project is to develop and validate novel reconstruction algorithms for MRE data using state-of-the-art Bayesian inversion techniques. The aim of these algorithms is to enable accurate reconstruction of tissue properties, and to quantify uncertainties in estimated/reconstructed properties. These algorithms will be tailored to improve significantly MRE-based diagnosis, by assimilating MRI data into a general class of heterogeneous and anisotropic biomechanical models. The validation of the algorithms developed with this project will be conducted with data acquired at the Sir Peter Mansfield Imaging Centre.