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: 0916526
• 负责人: Nikos Chrisochoides
• 金额: $ 50万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916526&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.

脑 MRI 实时图像到网格转换的新颖算法方法摘要：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），例如分别用于 2D 和 3D 图像的三角形和四面体。 在计算机辅助手术 (CAS) 中，这种曲面细分（或有限元网格）是患者特定生物力学和生物流体有限元 (FE) 模拟的关键组成部分。 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 I2M 转换。 具体来说，我们专注于基于有限元的非刚性配准方法，该方法使用患者特定的生物力学模型来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，满足与以下相关的附加要求：（1）非刚性配准的准确性 大脑图像，以及（2）神经外科或头颈手术施加的实时限制。这项工作的智力优点是开发了新颖的理论框架，该框架扩展了用于标量和并行保证质量 Delaunay 网格生成的现有点插入方法。 这些扩展提高了算法的灵活性，这对于满足保真度等特定于应用的要求非常重要。该提案将对 CAS 和计算机辅助设计 (CAD) 的多个领域产生更广泛的影响。 医学图像的非刚性配准是 CAS 中许多应用的一项支持技术，CAS 是医疗保健行业快速发展的领域。 我们的算法将有助于预防医疗错误和使用新的（更有效/更准确）的技术，这些技术可以产生有助于减少医疗和住院费用的产品（即图像引导神经导航系统）。 具体来说，（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来最大限度地减少神经功能缺损的可能性，从而改善患者的预后；（2）微创内窥镜手术可减少失血量并减少术后疼痛，从而使患者更快康复并更早出院。 此外，基于我们的 I2M 转换算法的技术可用于医疗模拟器，这可以改善医生培训并最大限度地减少医疗程序中的错误。