作者= Drakopoulos Fotis Tsolakis克里斯托,上地才气,刘,姚明呈君,Kavazidi Kyriaki Rafailia, Foroglou Nikolaos, Fedorov安德烈,Frisken莎拉,Kikinis罗恩格尔贝亚历山德拉,Chrisochoides Nikos TITLE =自适应图像引导Neuronavigation沉浸式系统基于物理非刚性配准杂志=数字医疗前沿体积= 2年= 2021 URL = //www.thespel.com/articles/10.3389/fdgth.2020.613608 DOI = 10.3389 / fdgth.2020.613608 ISSN = 267雷竞技rebat3 - 253 x文摘=目的:在图像引导神经外科,co-registered术前解剖、功能,和扩散张量成像可用于促进一个安全的切除脑肿瘤的雄辩的大脑区域。然而,大脑在手术过程中变形,特别是在肿瘤切除的存在。非刚性的登记(NRR)术前图像数据可以用来创建一个注册图像捕获的变形术中图像,同时保持术前图像的质量。使用临床数据,本文报告的结果的比较方法在几个非刚性配准的精度和性能处理大脑变形。一种新的自适应方法,自动删除网格元素的面积切除肿瘤,从而处理变形的切除。改善用户体验,我们也提出了一个新的使用方式混合现实与超声、MRI和CT。材料与方法:本研究着重于30神经胶质瘤手术在两个不同的医院,其中许多涉及到重大的切除肿瘤体积。一种自适应方法基于物理非刚性配准(A-PBNRR)注册为每个病人术前和术中磁共振成像。结果与其他三个现成的注册方法:严格的登记中实现三维切片机v4.4.0;实现b样条非刚性配准的三维切片机v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon.Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting.Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.