| Autor: |
He 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 B, Do S, Karl WC, Kambadakone A, Sahani DV, Pien H |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2010 May; Vol. 29 (5), pp. 1182-91. Date of Electronic Publication: 2010 Apr 08. |
| DOI: |
10.1109/TMI.2010.2043536 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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