AF:Small:A Novel Algorithmic Approach for Real-Time Image-to-Mesh Conversion of Brain MRI

一种新的实时脑MRI图像到网格转换的算法摘要：图像到网格(I2M)转换是将图像细分为更简单的几何形状(或元素)，如2D和3D图像的三角形和四面体。在计算机辅助外科手术(CAS)中，这种剖分(或有限元网格)是特定于患者的生物力学和生物流体有限元(FE)模拟的关键组成部分。在这个项目中，我们的目标是将I2M转换为图像引导的神经外科手术和内窥镜下的颈部和头部手术。具体地说，我们专注于基于有限元的非刚性配准方法，它使用患者特定的生物机械模型来融合术前手术中的脑图像(例如。这个项目的目标是扩展Delaunay精化算法和理论，以保证质量的I2M转换，满足与以下相关的额外要求：(1)非刚性脑图像配准的准确性，以及(2)神经外科或头颈部手术施加的实时约束。这项工作的智能优点是发展了新的理论框架，该框架扩展了现有的点插入方法，用于标量和并行保证质量的Delaunay网格生成。这些扩展增加了算法的灵活性，这对于满足特定应用程序的要求(如保真度)非常重要。该提案将对CAS和计算机辅助设计(CAD)的几个领域产生更广泛的影响。医学图像的非刚性配准是CAS中许多应用的一种使能技术，而CAS是医疗行业中一个快速发展的领域。我们的算法将有助于防止医疗差错和使用新的(更有效/更准确的)技术，这些技术可以产生有助于降低医疗和住院费用的产品(即图像引导神经导航系统)。具体地说，(1)图像引导神经外科手术提高了肿瘤切除的成功率，同时通过保留关键组织将神经缺陷的可能性降至最低，从而改善了患者的预后，(2)微创内窥镜手术减少了出血，减少了术后疼痛，从而使患者更快地恢复和更早地出院。此外，基于我们的I2M转换算法的技术可以用于医学模拟器，可以改善医生的培训，并将医疗程序中的错误降至最低。

A Novel Algorithmic Approach for Real-Time Image-to-Mesh Conversion of Brain MRIAbstract: Image-to-Mesh (I2M) conversion is the tessellation of images into simpler geometrical shapes (or elements) like triangles and tetrahedral for 2D and 3D images, respectively. In Computer Assisted Surgery (CAS) this tessellation (or finite element mesh) is a critical component for patient-specific bio-mechanics and bio-fluid finite element (FE) simulations. In this project, we target I2M conversion for image guided neurosurgery and endoscopic neck and head surgery. Specifically, we focus on FE-based non-rigid registration methods which use patient-specific bio-mechanical models to fuse pre-operative intra-operative brain images (eg. Magnetic Resonance Images and/or Computed Tomography Scans).The objective of this project is to extend the Delaunay refinement algorithms and theory for guaranteed quality I2M conversion that meets additional requirements related to: (1) accuracy of non-rigid registration of brain images, and (2) real-time constrains imposed by neurosurgery or head and neck surgery. The intellectual merit of this work is the development of novel theoretical framework which extends existing point insertion methods for both scalar and parallel guaranteed quality Delaunay mesh generation. These extensions increase algorithm flexibility which is important to satisfy application-specific requirements like fidelity.The proposal will have a broader impact on several areas in both CAS and Computer Aided Design (CAD). Non-rigid registration of medical images is an enabling technology for many applications in CAS which is a rapidly growing area in health care industry. Our algorithms will contribute in the prevention of medical errors and theuse of new (more effective/accurate) technologies which can lead to products (i.e., image guided neuro-navigation systems) that will help reduce medical and hospitalization expenses. Specifically, (1) image guided neurosurgery increases the percentage of successful tumor resections while minimizing the potential for neurological deficit by preserving critical tissue and hence improves prognosis for patient, and (2) minimally invasive endoscopic surgery results in less blood loss and reduced post-operative pain lead to faster recovery and earlier discharge of patients. In addition technology based on our I2M conversion algorithms can be used in medical simulators which can improve doctor training and minimize errors in medical procedures.