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A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification
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DOI：10.1109/TMI.2010.2043536.png)
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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.
Keyword:
Deconvolution
perfusion computed tomography (CT)
singular value decomposition
spatial and temporal correlations
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CT-perfusion imaging of the human brain:: Advanced deconvolution analysis using circulant singular value decomposition人脑的CT灌注成像:: 使用循环奇异值分解的高级反卷积分析
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