Gradient-Based Optimization for Poroelastic and Viscoelastic MR Elastography.
Gradient-Based Optimization for Poroelastic and Viscoelastic MR Elastography.
复制标题
基于梯度的毛弹性和粘弹性MR弹性图的优化。
DOI：
10.1109/tmi.2016.2604568
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发表时间：
2017-01
影响因子：
10.6
通讯作者：
Paulsen KD
中科院分区：
文献类型：
--
作者：
Tan L;McGarry MD;Van Houten EE;Ji M;Solamen L;Weaver JB;Paulsen KD
我们描述了一种有效的梯度计算，用于解决磁共振弹性成像（MRE）中出现的反演问题。该算法可以被视为基于拉格朗日公式的广义“伴随方法”。经典伴随法的要求之一是保证弹性问题中刚度矩阵的自伴随性。在本文中，我们表明这一性质不再是我们算法中的必要条件，但计算性能可以与经典方法一样高效，经典方法仅涉及两个前向解，并且与要估计的参数数量无关。该算法是使用多孔弹性和粘弹性建模在材料属性重建中开发和实施的。各种基于梯度和 Hessian 的优化技术已经在模拟、模型和体内大脑数据上进行了测试。数值结果表明了所提出的梯度计算方案的可行性和效率。
We describe an efficient gradient computation for solving inverse problems arising in magnetic resonance elastography (MRE). The algorithm can be considered as a generalized ‘adjoint method’ based on a Lagrangian formulation. One requirement for the classic adjoint method is assurance of the self-adjoint property of the stiffness matrix in the elasticity problem. In this paper, we show this property is no longer a necessary condition in our algorithm, but the computational performance can be as efficient as the classic method, which involves only two forward solutions and is independent of the number of parameters to be estimated. The algorithm is developed and implemented in material property reconstructions using poroelastic and viscoelastic modeling. Various gradient- and Hessian-based optimization techniques have been tested on simulation, phantom and in vivo brain data. The numerical results show the feasibility and the efficiency of the proposed scheme for gradient calculation.
影响因子：
3.7
作者：
Van Houten EE
通讯作者：
Van Houten EE