A spatio-temporal deconvolution method to improve perfusion CT quantification.
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.
He
Lili
L
Laboratory for Medical Imaging and Computations, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. lhe2@partners.org
Orten
Burkay
B
Do
Synho
S
Karl
W Clem
WC
Kambadakone
Avinish
A
Sahani
Dushyant V
DV
Pien
Homer
H
D001783:Blood Flow Velocity; D003198:Computer Simulation; D003287:Contrast Media; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010477:Perfusion; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000379:methods*
D003287:Contrast Media