Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems.
Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems.
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DOI：
10.3389/fdgth.2020.613608
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发表时间：
2020
影响因子：
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通讯作者：
Chrisochoides N
中科院分区：
文献类型：
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作者：
Drakopoulos F;Tsolakis C;Angelopoulos A;Liu Y;Yao C;Kavazidi KR;Foroglou N;Fedorov A;Frisken S;Kikinis R;Golby A;Chrisochoides N
目的：在图像引导神经外科中，术前解剖、功能和弥散张量成像的共配准可用于促进脑功能区脑肿瘤的安全切除。然而，大脑在手术过程中会变形，特别是在肿瘤切除的情况下。术前图像数据的非刚性配准（NRR）可用于创建配准图像，该配准图像捕获术中图像中的变形，同时保持术前图像的质量。使用临床数据，本文报告了几种非刚性配准方法处理脑变形的准确性和性能的比较结果。提出了一种新的自适应方法，该方法自动删除切除肿瘤区域中的网格元素，从而处理存在切除时的变形。为了改善用户体验，我们还提出了一种将混合现实与超声、MRI和CT结合使用的新方法。材料与方法：这项研究的重点是在两个不同的医院进行的30例胶质瘤手术，其中许多涉及切除显着的肿瘤体积。基于自适应物理的非刚性配准方法（A-PBNRR）可配准每位患者的术前和术中MRI。将结果与其他三种现成的配准方法进行比较：在3D Slicer v4.4.0中实施的刚性配准;在3D Slicer v4.4.0中实施的B样条非刚性配准;以及在ITKv4.7.0中实施的PBNRR，A-PBNRR基于该方法。采用三种措施来促进配准准确性的综合评价：（i）视觉评估，（ii）基于Hausdorff距离的度量，以及（iii）使用神经外科医生识别的解剖点的基于地标的方法。结果如下：与刚性和传统的基于物理学的非刚性配准相比，使用多组织网格自适应的A-PBNRR将变形配准的准确性提高了五倍以上，与ITK和3D Slicer的B样条插值方法相比提高了四倍。性能分析表明，A-PBNRR可以平均在<2分钟内应用，达到临床环境中使用的理想速度。结论：A-PBNRR方法在切除存在下建模变形方面的表现明显优于其他现成的配准方法。配准精度和性能证明足以在手术室中具有临床价值。与当前的神经导航系统相比，A-PBNRR与混合现实系统相结合，提供了一种功能强大且经济实惠的解决方案。
Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer 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.