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 the use 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.
