The parts of the human body affected by a disease do not only undergo structural changes but also demonstrate
significant physiological (functional) abnormalities. An important parameter that reveals the functional state of
tissue is the flow of blood per unit tissue volume or perfusion, which can be obtained using dynamic imaging
methods. One mathematical approach widely used for estimating perfusion from dynamic imaging data is based
on a convolutional tissue-flow model. In these approaches, deconvolution of the observed data is necessary to
obtain the important physiological parameters within a voxel. Although several alternatives have been proposed
for deconvolution, all of them treat neighboring voxels independently and do not exploit the spatial correlation
between voxels or the temporal correlation within a voxel over time. These simplistic approaches result in a noisy
perfusion map with poorly defined region boundaries. In this paper, we propose a novel perfusion estimation
method which incorporates spatial as well as temporal correlation into the deconvolution process. Performance
of our method is compared to standard methods using independent voxel processing. Both simulated and real
data experiments illustrate the potential of our method.
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