title: Hardware implementation of hierarchical volume subdivision-based elastic registration.
abstract: Real-time, elastic and fully automated 3D image registration is critical to the efficiency and effectiveness of many image-guided diagnostic and treatment procedures relying on multimodality image fusion or serial image comparison. True, real-time performance will make many 3D image registration-based techniques clinically viable. Hierarchical volume subdivision-based image registration techniques are inherently faster than most elastic registration techniques, e.g. free-form deformation (FFD)-based techniques, and are more amenable for achieving real-time performance through hardware acceleration. Our group has previously reported an FPGA-based architecture for accelerating FFD-based image registration. In this article we show how our existing architecture can be adapted to support hierarchical volume subdivision-based image registration. A proof-of-concept implementation of the architecture achieved speedups of 100 for elastic registration against an optimized software implementation on a 3.2 GHz Pentium III Xeon workstation. Due to inherent parallel nature of the hierarchical volume subdivision-based image registration techniques further speedup can be achieved by using several computing modules in parallel.
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA. dandekar@ieee.org
authors.forename: Omkar
authors.initials: O
authors.lastname: Dandekar
authors.forename: Vivek
authors.initials: V
authors.lastname: Walimbe
authors.forename: Raj
authors.initials: R
authors.lastname: Shekhar
mesh_terms: D001185:Artificial Intelligence; D004867:Equipment Design; D019544:Equipment Failure Analysis; D007089:Image Enhancement / Q000295:instrumentation* / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation* / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*