Linked Life Data

17881799

Source:http://linkedlifedata.com/resource/pubmed/id/17881799

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
19
pubmed:dateCreated
2007-9-20
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0031-9155
pubmed:author
pubmed:issnType
Print
pubmed:day
7
pubmed:volume
52
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5771-83
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration.
pubmed:affiliation
Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA.
pubmed:publicationType
Journal Article, Evaluation Studies