A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes
A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes
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DOI：
10.1080/21681163.2018.1523750
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发表时间：
2019-01-01
影响因子：
1.6
通讯作者：
Johnson, Chris R.
中科院分区：
文献类型：
--
作者：
Athawale, Tushar M.;Johnson, Kara A.;Johnson, Chris R.
脑深部电刺激（DBS）是治疗帕金森病等运动障碍患者的既定疗法。患者特定的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。计算模型使用诸如磁共振（MR）和计算机断层扫描（CT）的脑成像来确定患者头部内的DBS电极位置。然而，脑成像的有限分辨率引入了电极位置的不确定性。最佳患者反应的DBS刺激设置对DBS电极与特定神经基质（白色/灰质）的相对定位敏感。在我们的贡献中，我们研究了DBS电极中的位置不确定性，以有限的分辨率成像。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表征的DBS电极的几何形状。其次，我们设计了一个统计框架，用于量化DBS电极位置属性的不确定性，即纵轴方向和亚体素水平的接触中心位置。统计框架利用第一步中得出的分析模型和用于不确定性量化的贝叶斯概率模型。最后，接触中心位置的不确定性是通过体绘制和等值面技术交互式可视化。我们证明了我们的贡献，通过实验合成和真实的数据集的有效性。我们表明，在真正的电极位置的空间变化是显着的有限分辨率成像，和交互式可视化可以在探索概率的位置变化在DBS铅。
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.