Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TMI.2010.2043536 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
anatomic imaging,diagnostic monitoring,local arterial input function,computerised tomography,spatio-temporal deconvolution method,haemorheology,voxel segregation,ill-posed problem,spatial and temporal correlations,perfusion ct quantification,singular-value decomposition,tissue enhancement profile,deconvolution,image classification,perfusion computed tomography (ct),therapy monitoring,residue function,hemodynamic parameters,rectal cancer tumor images,singular value decomposition,medical image processing,perfusion imaging,haemodynamics,radiology,hemodynamics,rectal cancer,region of interest,convolution,computed tomography,helium,perfusion,computer simulation,biomedical imaging | Voxel,Nuclear medicine,Perfusion scanning,Medical imaging,Deconvolution,Artificial intelligence,Contextual image classification,Computer vision,Pattern recognition,Convolution,Image noise,Region of interest,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 5 | 1558-254X |
Citations | PageRank | References |
14 | 0.82 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lili He | 1 | 14 | 0.82 |
| Burkay Orten | 2 | 98 | 6.00 |
| Synho Do | 3 | 94 | 12.86 |
| William Clement Karl | 4 | 79 | 13.41 |
| Avinish Kambadakone | 5 | 14 | 0.82 |
| Dushyant V Sahani | 6 | 23 | 2.81 |
| Homer Pien | 7 | 44 | 6.48 |