Fovargue Daniel, Nordsletten David, Sinkus Ralph
Imaging Sciences & Biomedical Engineering, King's College London, London, UK.
Inserm U1148, LVTS, University Paris Diderot, University Paris 13, Paris, 75018, France.
NMR Biomed. 2018 Oct;31(10):e3935. doi: 10.1002/nbm.3935. Epub 2018 May 18.
Assessment of tissue stiffness is desirable for clinicians and researchers, as it is well established that pathophysiological mechanisms often alter the structural properties of tissue. Magnetic resonance elastography (MRE) provides an avenue for measuring tissue stiffness and has a long history of clinical application, including staging liver fibrosis and stratifying breast cancer malignancy. A vital component of MRE consists of the reconstruction algorithms used to derive stiffness from wave-motion images by solving inverse problems. A large range of reconstruction methods have been presented in the literature, with differing computational expense, required user input, underlying physical assumptions, and techniques for numerical evaluation. These differences, in turn, have led to varying accuracy, robustness, and ease of use. While most reconstruction techniques have been validated against in silico or in vitro phantoms, performance with real data is often more challenging, stressing the robustness and assumptions of these algorithms. This article reviews many current MRE reconstruction methods and discusses the aforementioned differences. The material assumptions underlying the methods are developed and various approaches for noise reduction, regularization, and numerical discretization are discussed. Reconstruction methods are categorized by inversion type, underlying assumptions, and their use in human and animal studies. Future directions, such as alternative material assumptions, are also discussed.
对于临床医生和研究人员来说，评估组织硬度是很有必要的，因为病理生理机制常常会改变组织的结构特性，这一点已得到充分证实。磁共振弹性成像（MRE）为测量组织硬度提供了一种方法，并且在临床应用方面有着悠久的历史，包括对肝纤维化进行分期以及对乳腺癌恶性程度进行分层。MRE的一个重要组成部分是重建算法，该算法通过解决反问题从波动图像中得出硬度。文献中已经提出了大量的重建方法，这些方法在计算成本、所需的用户输入、潜在的物理假设以及数值评估技术等方面存在差异。这些差异进而导致了准确性、稳健性和易用性的不同。虽然大多数重建技术已经在计算机模拟或体外模型中得到验证，但在实际数据上的性能往往更具挑战性，这凸显了这些算法的稳健性和假设。本文回顾了许多当前的MRE重建方法，并讨论了上述差异。阐述了这些方法背后的材料假设，并讨论了降噪、正则化和数值离散化的各种方法。重建方法按反演类型、潜在假设及其在人体和动物研究中的应用进行分类。还讨论了未来的发展方向，例如替代材料假设。
