College of Information Engineering, Shenzhen University, Shenzhen, 518060, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
BMC Med Inform Decis Mak. 2017 Dec 20;17(Suppl 3):172. doi: 10.1186/s12911-017-0560-z.
Cardiac diseases represent the leading cause of sudden death worldwide. During the development of cardiac diseases, the left ventricle (LV) changes obviously in structure and function. LV motion estimation plays an important role for diagnosis and treatment of cardiac diseases. To estimate LV motion accurately for cine magnetic resonance (MR) cardiac images, we develop an algorithm by combining point set matching with surface structure features of myocardium.
The structure features of myocardial wall are described by estimating the normal directions of points locating on the myocardium contours using an approximation approach. The Gaussian mixture model (GMM) of structure features is used to represent LV structure feature distribution. A new cost function is defined to represent the differences between two Gaussian mixture models, which are the GMM of structure features and the GMM of positions of two point sets. To optimize the cost function, its gradient is derived to use the Quasi-Newton (QN). Furthermore, to resolve the dis-convergence issue of Quasi-Newton for high-dimensional parameter space, Stochastic Gradient Descent (SGD) is used and SGD gradient is derived. Finally, the new cost function is solved by optimization combining SGD with QN. With the closed form expression of gradient, this paper provided a computationally efficient registration algorithm.
Three public datasets are employed to verify the performance of our algorithm, including cardiac MR image sequences acquired from 33 subjects, 14 inter-subject heart cases, and the data obtained in MICCAI 2009s 3D Segmentation Challenge for Clinical Applications. We compare our results with those of the other point set registration methods for LV motion estimation. The obtained results demonstrate that our algorithm shows inherent statistical robustness, due to the combination of SGD and Quasi-Newton optimization. Furthermore, our method is shown to outperform other point set matching methods in the registration accuracy.
We provide a novel effective algorithm for cardiac motion estimation by introducing LV surface structure feature to point set matching. A new cost function is defined to measure the discrepancy between GMMs of two point sets. The GMM of point positions and the GMM of surface structure descriptor are defined at the same time. Optimization by combining SGD and Quasi-Newton is performed to solve the cost function. We experimentally demonstrate that our algorithm shows improved registration accuracy, and is convergent when used in high-dimensional parameter space.
心脏疾病是全球范围内导致猝死的主要原因。在心脏疾病发展过程中，左心室（LV）在结构和功能上发生明显变化。LV 运动估计对于心脏疾病的诊断和治疗起着重要作用。为了准确估计电影磁共振（MR）心脏图像的 LV 运动，我们开发了一种算法，该算法将点集匹配与心肌表面结构特征相结合。
使用逼近方法估计位于心肌轮廓上的点的法向方向来描述心肌壁的结构特征。使用结构特征的高斯混合模型（GMM）来表示 LV 结构特征分布。定义新的代价函数来表示两个高斯混合模型之间的差异，这两个高斯混合模型分别是结构特征的 GMM 和两个点集位置的 GMM。为了优化代价函数，推导其梯度并使用拟牛顿（Quasi-Newton，QN）。此外，为了解决高维参数空间中拟牛顿的发散问题，使用随机梯度下降（Stochastic Gradient Descent，SGD）并推导 SGD 梯度。最后，通过结合 SGD 和 QN 的优化来求解新的代价函数。由于梯度的闭式表达式，本文提供了一种计算效率高的配准算法。
使用三个公共数据集验证我们算法的性能，包括从 33 个对象获得的心脏 MR 图像序列、14 个个体间心脏病例以及 MICCAI 2009 年临床应用 3D 分割挑战赛获得的数据。我们将我们的结果与其他用于 LV 运动估计的点集配准方法进行比较。获得的结果表明，由于 SGD 和拟牛顿优化的结合，我们的算法表现出固有的统计鲁棒性。此外，我们的方法在配准精度方面优于其他点集匹配方法。
通过将 LV 表面结构特征引入到点集匹配中，我们为心脏运动估计提供了一种新颖有效的算法。定义新的代价函数来测量两个点集的 GMM 之间的差异。同时定义点位置的 GMM 和表面结构描述符的 GMM。通过结合 SGD 和拟牛顿优化来求解代价函数。我们的实验结果表明，我们的算法提高了配准精度，并且在高维参数空间中是收敛的。
