http://impact.crhc.illinois.edu/ftp/conference/cf08.stone.pdf
Abstract
Computational acceleration on graphics processing units(GPUs) can make advanced magnetic resonance imaging(MRI) reconstruction algorithms attractive in clinical settings,thereby improving the quality of MR images across abroad spectrum of applications. At present, MR imaging isoften limited by high noise levels, signi cant imaging artifacts,and/or long data acquisition (scan) times. Advancedimage reconstruction algorithms can mitigate these limitationsand improve image quality by simultaneously operatingon scan data acquired with arbitrary trajectories and incorporatingadditional information such as anatomical constraints.However, the improvements in image quality comeat the expense of a considerable increase in computation.This paper describes the acceleration of an advanced reconstructionalgorithm on NVIDIA’s Quadro FX 5600. Optimizationssuch as register allocating the voxel data, tilingthe scan data, and storing the scan data in the Quadro’sconstant memory dramatically reduce the reconstruction’srequired bandwidth to o -chip memory. The Quadro’s specialfunctional units provide substantial acceleration of thetrigonometric computations in the algorithm’s inner loops,and experimentally-tuned code transformations increase thereconstruction’s performance by an additional 20%.
Authors
Sam S. Stone, Center for Reliable and High-Performance ComputingUniversity of Illinois at Urbana-Champaign
Justin P. Haldar, Department of Electrical andComputer EngineeringUniversity of Illinois atUrbana-Champaign
Stephanie C. Tsao, Wen-mei W. Hwu, Center for Reliable and High-Performance ComputingUniversity of Illinois at Urbana-Champaign
Zhi-Pei Liang, Department of Electrical andComputer EngineeringUniversity of Illinois atUrbana-Champaign
Bradley P. Sutton, Bioengineering DepartmentBiomedical Imaging Center,Beckman Institute forAdvanced Science andTechnologyUniversity of Illinois atUrbana-Champaign