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Perfusion MRI Deconvolution with Delay Estimation and Non-Negativity Constraints

Abstract : Perfusion MRI deconvolution aims to recover the time-dependent residual amount of indicator (residue function) from the measured arterial and tissue concentration time-curves. The deconvolution is complicated by the presence of a time lag between the measured concentrations. Moreover the residue function must be non-negative and its shape may become non-monotonic due to dispersion phenomena. We introduce Modified Exponential Bases (MEB) to perform de-convolution. The MEB generalize the previously proposed exponential approximation (EA) by taking into account the time lag and introducing non-negativity constraints for the recovered residue function also in the case of non-monotonic dispersed shapes, thus overcoming the limitation due to the non-increasing assumtion of the EA. The deconvolution problem is solved linearly. Quantitative comparisons with the widespread block-circulant Singular Value Decomposition show favorable results in recovering the residue function.
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https://hal.inria.fr/hal-01143213
Contributor : Marco Pizzolato <>
Submitted on : Thursday, April 16, 2015 - 11:31:44 PM
Last modification on : Thursday, March 5, 2020 - 4:48:42 PM
Long-term archiving on: : Tuesday, April 18, 2017 - 10:40:18 PM

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Marco Pizzolato, Aurobrata Ghosh, Timothé Boutelier, Rachid Deriche. Perfusion MRI Deconvolution with Delay Estimation and Non-Negativity Constraints. International Symposium on Biomedical Imaging, Apr 2015, Brooklyn, New York, United States. ⟨10.1109/ISBI.2015.7164057⟩. ⟨hal-01143213⟩

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