The goal of this project is to acquire and integrate Biomedical Computing Infrastructure (BCI) capable of processing the increasingly high-resolution, large-volume, and high-frequency digital content generated within biomedical applications. The BCI will comprise: a large NVIDIA processor-based Tesla cluster with double precision Graphics Processing Units (GPUs) along with a multi-node NEC Nehalem-based cluster to drive the Tesla cluster via Infiniband; large shared memory multi-core computer nodes; and a large parallel high-performance solid-state disk farm.
Intellectual Merit: While parallel- and grid-computing is relatively well understood, effective use of a cluster of massively multi-core GPUs with large memory, and fast disk access has as yet been minimally explored. Thus, the BCI seeks to facilitate deployment of this transformational computational paradigm in ongoing biomedical research projects between the University at Buffalo, SUNY and the Roswell Park Cancer Institute. These projects encompass the gamut of biomedical computing from: virtual surgery and intervention; image segmentation and labeling; computer tomography and reconstruction; imaging biomarkers and computer-aided diagnosis; to nuclear molecular imaging.
Broader Impact: This BCI empowers a large group of multidisciplinary researchers to unlock the full potential of the digital content in the biomedical enterprise as well as attain faster and more reliable transfer of science from the lab to the clinic. In addition, a vibrant dissemination and outreach effort has been planned around the BCI, involving classes, tutorials and workshops, to engage students and researchers of all ages. Many of these activities forming the foundation of the team?s outreach efforts, ranging from the high-school summer institutes to conference workshops, have already been initiated and the web-portal documents these efforts (www.cse.buffalo.edu/~vipin/nsf/cri2009/).
LUMBAR CAD Our Computer Aided Diagnosis system for lumbar area, LumbarCAD, aims at automatingthe various routine diagnosis steps within the work flow of the clinical roradiologist. As shown in Fig. 1, four major seamless components are under work. Most of research is within the ‘safeCAD box’ that presents the radiologists with accurate, robust, and reproducible diagnosis results. We presented our view of the overall systemand how our lumbarCAD seamlessly integrates within the work flow of the clinical diagnosis with emphasis on the increased utilization of radiologist time and diagnosis reproducibility that decreases inter- and intra-radiologist disagreement. ROBO PLAN Our RoboPlan virtual intervention system will provide a comprehensive virtual open surgeryplatform with surgical training and assessment capabilities. This versatile tool will enablemedical trainees to get realistic hands-on experience in a range of patient specific surgicalprocedures before operating on real patients by providing real-time haptic/tactile and visualfeedback.RoboPlan will provide capabilities for pre-operative planning, 3D visualization, and intelligentsurgical guidance based on pre-existing data and critical anatomical features. In addition, Roboplan’s assessment feature will provide standardized feedback and evaluation scoresallowing for competence based advancement of residents.RoboPlan will provide an efficient, inexpensive, multi-use solution that will complement thecurrent training methods in Orthopedic Surgery and will overcome the limitations inherent in thetraditional approaches and currently available training systems.
