IEEE Trans Med Imaging. 2024 Mar;43(3):1138-1148. doi: 10.1109/TMI.2023.3329293. Epub 2024 Mar 5.
The inverse problem that underlies Magnetic Resonance Elastography (MRE) is sensitive to the measurement data and the quality of the results of this tissue elasticity imaging process can be influenced both directly and indirectly by measurement noise. In this work, we apply a coupled adjoint field formulation of the viscoelastic constitutive parameter identification problem, where the indirect influence of noise through applied boundary conditions is avoided. A well-posed formulation of the coupled field problem is obtained through conditions applied to the adjoint field, relieving the computed displacement field from kinematic errors on the boundary. The theoretical framework for this formulation via a nearly incompressible, parallel subdomain-decomposition approach is presented, along with verification and a detailed exploration of the performance of the methods via a numerical simulation study. In addition, the advantages of this novel approach are demonstrated in-vivo in the human brain, showing the ability of the method to obtain viable tissue property maps in difficult configurations, enhancing the accuracy of the method.
磁共振弹性成像（MRE）所基于的反问题对测量数据很敏感，并且组织弹性成像过程的结果质量可能会受到测量噪声的直接和间接影响。在这项工作中，我们应用了粘弹性本构参数识别问题的耦合伴随场公式，其中通过施加边界条件避免了噪声的间接影响。通过施加到伴随场的条件，可以得到一个良好的耦合场问题公式，从而使计算得到的位移场摆脱边界上的运动学误差。本文通过近不可压缩、并行子域分解方法提出了这种公式的理论框架，并通过数值模拟研究对方法的性能进行了验证和详细探讨。此外，该新方法在人体大脑中的体内应用也展示了其优势，证明了该方法在困难配置下获取可行的组织属性图的能力，提高了方法的准确性。
