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Perfusion MRI Deconvolution with Delay Estimation and Non-Negativity Constraints
Marco Pizzolato, Aurobrata Ghosh, Timothé Boutelier, Rachid Deriche

Perfusion MRI Deconvolution with Delay Estimation and Non-Negativity Constraints

Science & Mathematics Published 2015 Verification Pending

Overview & Synopsis

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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Document Information

Document Title Perfusion MRI Deconvolution with Delay Estimation and Non-Negativity Constraints
Author / Organization Marco Pizzolato, Aurobrata Ghosh, Timothé Boutelier, Rachid Deriche
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2015
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-159708
Original Source Link Visit Source Page on hal.science »