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位移体素化以解决网格-图像不匹配问题:在推导密集白质纤维应变中的应用

Displacement voxelization to resolve mesh-image mismatch: Application in deriving dense white matter fiber strains.

作者信息

Ji Songbai, Zhao Wei

机构信息

Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA; Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.

Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA.

出版信息

Comput Methods Programs Biomed. 2022 Jan;213:106528. doi: 10.1016/j.cmpb.2021.106528. Epub 2021 Nov 13.

Abstract

BACKGROUND AND OBJECTIVE

It is common to combine biomechanical modeling and medical images for multimodal analyses. However, mesh-image mismatch may occur that prevents direct information exchange. To eliminate mesh-image mismatch, we develop a simple but elegant displacement voxelization technique based on image voxel corner nodes to achieve voxel-wise strain. We then apply the technique to derive dense white matter fiber strains along whole-brain tractography (∼35 k fiber tracts consisting of ∼3.3 million sampling points) resulting from head impact.

METHODS

Displacements at image voxel corner nodes are first obtained from model simulation via scattered interpolation. Each voxel is then scaled linearly to form a unit hexahedral element. This allows convenient and efficient voxel-wise strain tensor calculation and displacement interpolation at arbitrary fiber sampling points via shape functions. Fiber strains from displacement interpolation are then compared with those from the commonly used strain tensor projection using either voxel- or element-wise strain tensors.

RESULTS

Based on a synthetic displacement field, fiber strains interpolated from voxelized displacement are considerably more accurate than those from strain tensor projection relative to the prescribed ground-truth (determinant of coefficient (R) of 1.00 and root mean squared error (RMSE) of 0.01 vs. 0.87 and 0.10, respectively). For a set of real-world reconstructed head impacts (N = 53), the strain tensor projection method performs similarly poorly (R of 0.80-0.90 and RMSE of 0.03-0.07), with overestimation strongly correlated with strain magnitude (Pearson correlation coefficient >0.9). Up to ∼15% of the fiber strains are overestimated by more than the lower bound of a conservative injury threshold of 0.09. The percentage increases to ∼37% when halving the threshold. Voxel interpolation is also significantly more efficient (15 s vs. 40 s for element strain tensor projection, without parallelization).

CONCLUSIONS

Voxelized displacement interpolation is considerably more accurate and efficient in deriving dense white matter fiber strains than strain tensor projection. The latter generally overestimates with overestimation magnitude strongly correlating with fiber strain magnitude. Displacement voxelization is an effective technique to eliminate mesh-image mismatch and generates a convenient image representation of tissue deformation. This technique can be generalized to broadly facilitate a diverse range of image-related biomechanical problems for multimodal analyses. The convenient image format may also promote and facilitate biomechanical data sharing in the future.

摘要

背景与目的

将生物力学建模与医学图像相结合进行多模态分析很常见。然而,可能会出现网格与图像不匹配的情况,从而阻碍直接的信息交换。为消除网格与图像的不匹配,我们基于图像体素角节点开发了一种简单而巧妙的位移体素化技术,以实现体素级应变。然后,我们应用该技术来推导因头部撞击而产生的全脑纤维束成像(约35,000条纤维束,由约330万个采样点组成)中的密集白质纤维应变。

方法

首先通过散射插值从模型模拟中获取图像体素角节点处的位移。然后将每个体素进行线性缩放,以形成一个单位六面体单元。这使得通过形状函数在任意纤维采样点进行方便且高效的体素级应变张量计算和位移插值成为可能。然后将通过位移插值得到的纤维应变与使用体素级或单元级应变张量的常用应变张量投影得到的纤维应变进行比较。

结果

基于一个合成位移场,相对于规定的真实值,从体素化位移插值得到的纤维应变比从应变张量投影得到的纤维应变要精确得多(决定系数(R)分别为1.00和均方根误差(RMSE)为0.01,而后者分别为0.87和0.10)。对于一组实际重建的头部撞击(N = 53),应变张量投影方法的表现同样不佳(R为0.80 - 0.90,RMSE为0.03 - 0.07),高估与应变大小强烈相关(皮尔逊相关系数>0.9)。高达约15%的纤维应变被高估超过保守损伤阈值0.09的下限。当阈值减半时,该百分比增加到约37%。体素插值效率也显著更高(15秒,而单元应变张量投影为40秒,未并行化)。

结论

在推导密集白质纤维应变方面,体素化位移插值比应变张量投影更精确、更高效。后者通常会高估,高估幅度与纤维应变大小强烈相关。位移体素化是消除网格与图像不匹配的有效技术,并生成组织变形的方便图像表示。该技术可广泛推广,以促进解决各种与图像相关的生物力学问题进行多模态分析。这种方便的图像格式未来也可能促进生物力学数据共享。

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