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

AF:Small：脑 MRI 实时图像到网格转换的新颖算法方法

• 批准号: 1139864
• 负责人: Nikos Chrisochoides
• 金额: $ 48.11万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1139864&HistoricalAwards=false
• 关键词: AF; Small; Novel; Algorithmic; Approach

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.

一种新的脑MRI实时图像到网格转换算法摘要：图像到网格（I2 M）转换 是图像的镶嵌 更简单的几何图形 形状（或 元素）一样 三角形和四面体分别用于2D和3D图像。 在计算机辅助手术（CAS）中，这种镶嵌（或有限元网格）是患者特定生物力学和生物流体有限元（FE）模拟的关键组件。 在这个项目中，我们的目标是图像引导神经外科手术和内窥镜颈部和头部手术的I2 M转换。 具体而言，我们专注于基于FE的非刚性配准方法，该方法使用患者特定的 生物力学模型融合术前术中脑图像（例如， 磁共振图像和/或计算机断层扫描）。该项目的目标是扩展Delaunay细化算法和理论，以保证质量 I2m 转换符合 与以下方面有关的额外要求：（1）非刚性配准的准确性 大脑图像的实时性，以及（2）神经外科或头颈外科手术的实时性限制。这项工作的智力价值是新的理论框架，扩展现有的点插入方法的标量和并行保证质量的Delaunay网格生成的发展。 这些扩展增加了算法的灵活性，这对于满足特定应用的要求（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，CAS是一个快速发展的领域， 医疗保健行业。 我们的算法将有助于预防医疗错误和使用新的（更有效/准确）技术，这些技术可以导致产品（即，图像引导神经导航系统），这将有助于减少医疗和住院费用。 具体而言，（1）图像引导的神经外科手术增加了成功切除肿瘤的百分比，同时通过保留关键组织最大限度地减少了神经功能缺损的可能性，因此改善了患者的预后，（2）微创内窥镜手术减少了失血量，减少了术后疼痛，使患者恢复更快，出院更早。 此外，基于我们的I2 M转换算法的技术可用于医疗模拟器，从而改善医生培训并最大限度地减少医疗程序中的错误。