GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

G. C. Sharp, N. Kandasamy, H. Singh, M. Folkert

Research output: Contribution to journalArticle

160 Scopus citations

Abstract

This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup - up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration.

Original languageEnglish (US)
Article number003
Pages (from-to)5771-5783
Number of pages13
JournalPhysics in medicine and biology
Volume52
Issue number19
DOIs
StatePublished - Sep 21 2007

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration'. Together they form a unique fingerprint.

  • Cite this