A finite element method to correct deformable image registration errors in low-contrast regions.
A finite element method to correct deformable image registration errors in low-contrast regions.
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DOI：
10.1088/0031-9155/57/11/3499
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发表时间：
2012-06-07
影响因子：
3.5
通讯作者：
Chetty IJ
中科院分区：
文献类型：
--
作者：
Zhong H;Kim J;Li H;Nurushev T;Movsas B;Chetty IJ
图像引导的自适应放射治疗需要可变形图像配准以在图像之间来回映射辐射剂量。本研究的目的是开发一种新的方法，以提高基于强度的图像配准算法在低对比度区域的准确性。在这项研究中，已经开发了一个计算框架，以提高质量的“恶魔”注册。对于配准的目标图像中的每个体素，计算该体素的邻域中的图像强度的标准偏差。基于高对比度区域的标准偏差生成高对比度区域的掩模。在掩蔽区域中，递归地细化四面体网格，使得这些区域中足够数量的四面体节点可以被选择作为驱动节点。采用有限元法（FEM）建立了由选定节点位移驱动的弹性系统，并在精细网格上实现。这些驱动节点的位移是用“恶魔”算法生成的。该系统的解决方案是使用共轭梯度法，并插值生成一个位移矢量场的配准图像。对肺部和前列腺患者的CT图像进行了FEM校正方法与“demons”算法的比较。基于变形图的物理性质分析了与“恶魔”配准相关的FEM校正性能，并通过专门为此研究开发的基准模型进行了定量评估。与基准模型相比，“恶魔”配准的最大误差为1.2 cm，可以通过有限元方法校正为0.4 cm，“恶魔”配准的平均误差从0.17 cm减小到0.11 cm。对于肺部和前列腺患者的CT图像，发现由“恶魔”算法生成的变形图在几个地方是不现实的。在这些地方，“恶魔”注册和他们的有限元校正之间的位移差异被发现在0.4厘米和1.1厘米的范围内。网格细化和有限元模拟在一个单线程的应用程序，需要约45分钟的计算时间在2.6 GH计算机上实现。这项研究表明，有限元方法可以集成与基于强度的图像配准算法，以提高其配准精度，特别是在低对比度区域。
Image-guided adaptive radiotherapy requires deformable image registration to map radiation dose back and forth between images. The purpose of this study is to develop a novel method to improve the accuracy of an intensity-based image registration algorithm in low-contrast regions. A computational framework has been developed in this study to improve the quality of the “demons” registration. For each voxel in the registration’s target image, the standard deviation of image intensity in a neighborhood of this voxel was calculated. A mask for high-contrast regions was generated based on their standard deviations. In the masked regions, a tetrahedral mesh was refined recursively so that a sufficient number of tetrahedral nodes in these regions can be selected as driving nodes. An elastic system driven by the displacements of the selected nodes was formulated using a finite element method (FEM) and implemented on the refined mesh. The displacements of these driving nodes were generated with the “demons” algorithm. The solution of the system was derived using a conjugated gradient method, and interpolated to generate a displacement vector field for the registered images. The FEM correction method was compared with the “demons” algorithm on the CT images of lung and prostate patients. The performance of the FEM correction relating to the “demons” registration was analyzed based on the physical property of their deformation maps, and quantitatively evaluated through a benchmark model developed specifically for this study. Compared to the benchmark model, the “demons” registration has the maximum error of 1.2 cm, which can be corrected by the FEM method to 0.4 cm, and the average error of the “demons” registration is reduced from 0.17 cm to 0.11 cm. For the CT images of lung and prostate patients, the deformation maps generated by the “demons” algorithm were found unrealistic at several places. In these places, the displacement differences between the “demons” registrations and their FEM corrections were found in the range of 0.4 cm and 1.1cm. The mesh refinement and FEM simulation were implemented in a single thread application which requires about 45 minutes of computation time on a 2.6 GH computer. This study has demonstrated that the finite element method can be integrated with intensity-based image registration algorithms to improve their registration accuracy, especially in low-contrast regions.
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DOI：
10.1080/10255840500295852
发表时间：
2005-06-01
影响因子：
1.6
作者：
Zhong, Hualiang;Wachowiak, Mark P.;Peters, Terry M.
通讯作者：
Peters, Terry M.
影响因子：
3.5
作者：
CHRISTENSEN, GE;RABBITT, RD;MILLER, MI
通讯作者：
MILLER, MI
影响因子：
10.6
作者：
Christensen, GE;Rabbitt, RD;Miller, MI
通讯作者：
Miller, MI
影响因子：
10.9
作者：
Thirion, J P
通讯作者：
Thirion, J P
影响因子：
19.5
作者：
BARRON, JL;FLEET, DJ;BEAUCHEMIN, SS
通讯作者：
BEAUCHEMIN, SS