Approach-specific, multi-GPU, multi-tool, high-realism neurosurgery simulation

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$1,932,231.00
Award Year:
2013
Program:
SBIR
Phase:
Phase II
Contract:
9R44OD018334-03A1
Award Id:
n/a
Agency Tracking Number:
R44OD018334
Solicitation Year:
2013
Solicitation Topic Code:
OD
Solicitation Number:
PAR09-220
Small Business Information
28 CORPORATE DRIVE, SUITE 204, CLIFTON PARK, NY, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
10926207
Principal Investigator:
ANDINET ENQUOBAHRIE
(518) 371-3971
andinet.enqu@kitware.com
Business Contact:
VICKI RAFFERTY
(518) 371-2971
contracts@kitware.com
Research Institution:
Stub




Abstract
DESCRIPTION (provided by applicant): Approach-specific, Multi-GPU, Multi-tool, High-realism Neurosurgery Simulation Surgical treatment of neurological diseases is complicated and requires advanced training. However, compressed internship schedules in hospitals creates a challenge to young surgeons to master techniques needed for such complex procedures. Surgical simulators are powerful tools to provide advanced training for complex procedures; accelerate the training of residents without a penalty in morbidity and mortality; and improve skills where patient outcome clearly correlates with surgical experience. Surgical simulation training has been proposed for nearly a decade, and advancement in computing technology and algorithm development has now made ita more realistic training option. Double-blind validation studies have shown that surgical residents trained with a simulator systematically outperformed residents without such training in performing complex procedures such as laparoscopic cholecystectomies. However, despite demonstrated advantages of using simulation in conjunction with traditional frameworks, simulation has only made limited inroads in surgical education, particularly in the field of neurosurgery. The overall goal of this proposal is todevelop and evaluate an interactive neurosurgery simulator that is clinically realistic and well-validated. During Phase I, we implemented a prototype interactive neurosurgery simulator for training medical students on the resection of deep seated tumors in the brain stem. The simulator consists of a multi-resolution, sulcal- separable meshing algorithm for pterional approach, an efficient and physically realistic non- linear model FEM formulation, and GPU acceleration. Our clinical collaborators deemed ourPhase 1 simulator a success for major components of tumor resection procedures. However, they also noted that it would benefit from consideration of vasculature, as manipulation of vasculature is critical to tumor resection and many other neurosurgical procedures. In this Phase 2 project, we propose to extend and refine our simulator with additional components required to simulate vasculature and build a simulator application for surgical treatment of arteriovenous malformations (AVMs), one of the most complex surgeries involving brain vasculature. We will conduct a validity study of the neurosurgery simulator in collaboration with neurosurgeons in the Department of Neurosurgery at the University of North Carolina. The interactive simulator that will be developed will facilitae training in neurosurgical procedures that are complicated and require advanced training. This will translate to fewer operating room errors, reduced patient morbidity and improved patient outcomes. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: Approach-specific, Multi-GPU, Multi-tool, High-realism Neurosurgery Simulation Surgical mistakes have catastrophic consequences. Therefore, surgeons use all tools available in their arsenal to avoid these errors. Surgical simulation, one such tool, allows young residents and experts to practice and advance their surgical skills in a risk-free environment. This project aims to develop and validate an interactive simulator for neurosurgical procedures that are complicated and require advanced training. This will translate to fewer operating room errors, reduced patient morbidity and improved patient outcomes.

* information listed above is at the time of submission.

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