Cross validation study of deep brain stimulation targeting: From experts to atlas-based, segmentation-based and automatic registration algorithms
Cross validation study of deep brain stimulation targeting: From experts to atlas-based, segmentation-based and automatic registration algorithms
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DOI：
10.1109/tmi.2006.882129
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发表时间：
2006-11-01
影响因子：
10.6
通讯作者：
Thiran, Jean-Philippe
中科院分区：
文献类型：
--
作者：
Castro, F. Javier Sanchez;Pollo, Claudio;Thiran, Jean-Philippe
图像配准算法的验证是一项艰巨的任务和开放式的问题，通常依赖于应用。在本文中，我们专注于脑深部电刺激（DBS）靶向治疗运动障碍，如帕金森病和原发性震颤。DBS涉及在大脑深处植入电极，以电刺激特定区域，从而关闭疾病症状。丘脑底核（subthalamic nucleus，简称丘脑底核）是这种手术的最佳靶点。不幸的是，在常见的医学成像模式中，通常不能清楚地区分脑白质。推断其位置的基本技术是使用解剖图谱和可见的周围地标。外科医生必须在术中使用电生理记录和宏刺激测试来调整电极。我们构建了一个来自特定患者的地面实况，这些患者的STNs在磁共振（MR）T2加权图像上清晰可见。选择患者作为右侧和左侧的图谱。然后，通过使用不同的方法将每个患者与图谱配准，获得了对心脏位置的几个估计。使用我们提出的验证方案驱动两项研究。首先，比较不同的图集为基础的和非刚性的注册算法，其性能和可用性的评估，以自动定位的血管。第二，研究哪些可见的周围结构影响的位置。这两项研究是交叉验证他们之间和对专家的可变性。使用该方案，我们评估了专家的能力，对所提供的测试算法的估计误差，我们证明了，自动定位是可能的，准确的专家驱动的技术，目前使用的。我们还展示了哪些结构必须考虑到准确估计的位置。
Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.