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About the CRTC About the CRTC

Mesh Generation

Mesh Generation 

  • Massive Parallel Mesh Generation for large scale Finite Element computations

 Brain Parallel Mesh Generation        

        Big Brain Data                              Chessapeak Bay                   Large Scale Aerodynamics CFD 

  • Real-Time Image-to-Mesh (I2M) Conversion for Medical Image Computing


                                                    I2M Conversion for Brain Tumor             Nidus for AVM Simulation                                                                    

  • Domain Specific Application Composition Environment 


Front Page

Parallel FE Mesh Generation

Image-to-Mesh (I2M) Conversion

Real-Time Medical Image Computing

Application Composition Environment

Parallel Software Runtime Systems

Telescopic Exascale-Era Mesh Generation

STEM Education

An Analytics-Driven Approach

The CRTC is a research laboratory in the Department of Computer Science at Old Dominion University and The College of William and Mary. The core mission of the CRTC is the development of disruptive technologies for managing large-scale parallel computations and imaging data for all types of image-driven engineering applications and real-time biomedical analysis for image-guided therapy, diagnosis and calibration of navigation systems and medical sensors. The CRTC group collaborates with groups like Harold F. Young Neurosurgical Center at VCU’s Medical Center and EVMS within Virginia and nation-wide with groups like Surgical Planning Laboratory in Brigham and Women's Hospital at the Harvard Medical School, as well as around the world with top Neurosurgical clinics and universities like Neurosurgical Department of Huashan Hospital Shanghai Medical College, Fudan University.



Copyright (C) 2016 CRTC Group for all content unless stated otherwise.

CRTC Overview

A. Mission: 

  • Maintain high-quality application-driven research environment to train both graduate and REU students to be highly competitive and productive within multi-disciplinary settings.  
  • Create   learning opportunities to motivate and excite undergraduate and high-school students to pursue studies in STEM education.
  • Initiate entrepreneurial opportunities to transfer technology from my lab to industry and create new opportunities for funding our basic research in challenging and emerging areas like image-driven modeling. 

CRTC's  PhD students are placed or recruited at top research medical schools and groups in US (eg. Harvard and NIH), research labs (LANL, PNNL, MPI in Germany) and companies (Dassault Systems, Ansys, Synopsys, MSC, Corvid Technologies, Broncus Inc. and Alter).  Given this record and extensive network of collaborations that span across four continents, nine countries and 17 Universities and Medical Schools we are well positioned to continue and improve our record in: innovation, productivity, outreach and entrepreneurial activities.  CRTC's innovation and publications record is second to none when it comes to  productivity of technologies that work in parallel mesh generation and real-time FE-based medical image computing.


B. Strategic Planning

CRTC's long term strategic planning is aligned:

  • with our nation’s priorities to reduce cost in healthcare without compromising quality and in many case improve quality, 
  • with NIA's and NASA/Langley priorities to help contribute in modeling and simulation to predict aerodynamic characteristics of configurations at conditions that cannot be simulated in ground test facilities, or safely tested in flight and thus contribute in national security and competiveness of our transportation and defense industries and
  • with our nations priority to invest in  e-learning technologies and in CRTC's case we target the vital area  of STEM K-12 education by developing a niche for geometry where we can  leverage our background and record in Engineering Geometry. 


C. Cross-cutting research with emphasis in Biomedicine/Healthcare, HPC Runtime Software Systems, and STEM Education 

In addition to our work on enabling technologies for Image Guided Neurosurgery for brain cancer with Harvard Medical School and Fudan University, Deep Brain Stimulation (DBS) for Parkinson’s disease with VCU and Endoscopic Sinus/Skullbase Surgery with EVMS, CRTC's technologies for non-rigid registration and real-time medical image fusion and I2M conversion are used in many applications in Image Guided Diagnosis and Therapy,  Life Sciences and engineering applications. We actively pursue such interdisciplinary opportunities within Virginia, US and abroad.  In the area of STEM education, our initial focus is on developing a framework for performing empirical studies on the effects of video lectures on K-12 students learning. 


Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection

Our recent work to improve the accuracy of non-rigid registration while maintaining its completion within the neurosurgery time constrains published in Frontiers in Neuroinform., 17 February 2014. The new method can reduce the alignment error up to seven and five times compared to a rigid and ITK's physic-based non-rigid registration methods, respectively. On average, the alignment error of the new method is reduced by 9.23 and 5.63 mm compared to the alignment error from the rigid and PBNRR method implemented in ITK.