Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 29, Issue 5, Pages 1182-1191Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2010.2043536
Keywords
Deconvolution; perfusion computed tomography (CT); singular value decomposition; spatial and temporal correlations
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Funding
- Center for Integration of Medicine and Innovative Technology (CIMIT)
- U.S. Army Medical Research
- National Science Foundation (NSF) Engineering Research Centers (ERC)
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Perfusion imaging is a useful adjunct to anatomic imaging in numerous diagnostic and therapy-monitoring settings. One approach to perfusion imaging is to assume a convolution relationship between a local arterial input function and the tissue enhancement profile of the region of interest via a residue function and subsequently solve for this residue function. This ill-posed problem is generally solved using singular-value decomposition based approaches, and the hemodynamic parameters are solved for each voxel independently. In this paper, we present a formulation which incorporates both spatial and temporal correlations, and show through simulations that this new formulation yields higher accuracy and greater robustness with respect to image noise. We also show using rectal cancer tumor images that this new formulation results in better segregation of normal and cancerous voxels.
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