A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes.

Pub Date : 2019-01-01 Epub Date: 2018-10-09 DOI:10.1080/21681163.2018.1523750
Tushar M Athawale, Kara A Johnson, Christopher R Butson, Chris R Johnson
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引用次数: 8

Abstract

Deep brain stimulation (DBS) is an established therapy for treating patients with movement disorders such as Parkinson's disease. Patient-specific computational modelling and visualisation have been shown to play a key role in surgical and therapeutic decisions for DBS. The computational models use brain imaging, such as magnetic resonance (MR) and computed tomography (CT), to determine the DBS electrode positions within the patient's head. The finite resolution of brain imaging, however, introduces uncertainty in electrode positions. The DBS stimulation settings for optimal patient response are sensitive to the relative positioning of DBS electrodes to a specific neural substrate (white/grey matter). In our contribution, we study positional uncertainty in the DBS electrodes for imaging with finite resolution. In a three-step approach, we first derive a closed-form mathematical model characterising the geometry of the DBS electrodes. Second, we devise a statistical framework for quantifying the uncertainty in the positional attributes of the DBS electrodes, namely the direction of longitudinal axis and the contact-centre positions at subvoxel levels. The statistical framework leverages the analytical model derived in step one and a Bayesian probabilistic model for uncertainty quantification. Finally, the uncertainty in contact-centre positions is interactively visualised through volume rendering and isosurfacing techniques. We demonstrate the efficacy of our contribution through experiments on synthetic and real datasets. We show that the spatial variations in true electrode positions are significant for finite resolution imaging, and interactive visualisation can be instrumental in exploring probabilistic positional variations in the DBS lead.

Abstract Image

Abstract Image

Abstract Image

脑深部刺激电极位置不确定性的量化和可视化统计框架。
脑深部电刺激(DBS)是一种治疗帕金森病等运动障碍的成熟疗法。患者特异性计算建模和可视化已被证明在DBS的手术和治疗决策中发挥关键作用。计算模型使用脑成像,如磁共振(MR)和计算机断层扫描(CT),来确定DBS电极在患者头部内的位置。然而,脑成像的有限分辨率引入了电极位置的不确定性。DBS刺激的最佳患者反应设置对DBS电极对特定神经基质(白质/灰质)的相对定位很敏感。在我们的贡献中,我们研究了DBS电极在有限分辨率成像中的位置不确定性。在一个三步的方法中,我们首先推导出一个封闭形式的数学模型来表征DBS电极的几何形状。其次,我们设计了一个统计框架来量化DBS电极位置属性的不确定性,即纵向轴的方向和接触中心在亚体素水平上的位置。统计框架利用第一步导出的分析模型和贝叶斯概率模型进行不确定性量化。最后,接触中心位置的不确定性通过体绘制和等表面技术交互式可视化。我们通过在合成和真实数据集上的实验证明了我们的贡献的有效性。我们发现,在有限分辨率成像中,真实电极位置的空间变化是重要的,交互式可视化可以用于探索DBS导联的概率位置变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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