Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model.

Ruizhi Liao,Alexandra J Golby,Yogesh Rathi,Shun Gong,Zhenrui Chen,Laura Rigolo,Ofer Pasternak,Lauren J O’Donnell,Lipeng Ning

doi:10.1016/j.nicl.2017.06.027

Abstract

Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography.We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography.We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema.Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors.

Highlights

A principal goal of modern surgical treatment for brain tumors is to maximize tumor removal while minimizing damage to critical areas of functioning brain (Sanai and Berger, 2008; McGirt et al, 2009)
We show that the challenge of edematous white matter (WM) fiber reconstruction in clinical data can be addressed to some extent by unscented Kalman filter (UKF) tractography with a two-tensor model (Malcolm et al, 2010), and we analyze the performance of the method
We note that the default parameters of UKF tractography work reasonably well in edema, and we have shown that the model performs significantly better than the singletensor model in a small study of our patient data (Chen et al, 2015, 2016)

Summary

Introduction

A principal goal of modern surgical treatment for brain tumors is to maximize tumor removal while minimizing damage to critical areas of functioning brain (Sanai and Berger, 2008; McGirt et al, 2009). The singletensor model is not able to represent complex WM configurations, such as fiber crossings or partial volume effects (Alexander et al, 2001; Tuch et al, 2002) Due primarily to this limitation, tractography analyses based on DTI underestimate the full anatomical extent of fiber tracts (Kinoshita et al, 2005; Le Bihan et al, 2006; Duffau, 2014; Feigl et al, 2014). Some improvement can be achieved by DTI tractography approaches that employ information from the previous step (Weinstein et al, 1999; Lazar et al, 2003; Westin et al, 2002) during fiber tracking These approaches improve anatomical accuracy of tractography for neurosurgical planning (Feigl et al, 2014), though they still suffer from the basic limitations of the DTI model.

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Conclusion