Nonlinear Stochastic Regularization to Characterize Tissue Residue Function in Bolus-Tracking MRI: Assessment and Comparison With SVD, Block-Circulant SVD, and Tikhonov

An accurate characterization of tissue residue function R ( t ) in bolus-tracking magnetic resonance imaging is of crucial importance to quantify cerebral hemodynamics. R ( t ) estimation requires to solve a deconvolution problem. The most popular deconvolution method is singular value decomposition...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2009-05, Vol.56 (5), p.1287-1297
Main Authors: Zanderigo, Francesca, Bertoldo, Alessandra, Pillonetto, Gianluigi, Cobelli$^{\ast}$, Claudio
Format: Article
Language:English
Subjects:
Algorithms
Biomedical imaging
Biomedical measurements
Blood flow
Blood Flow Velocity - physiology
Blood Volume - physiology
Brain - blood supply
Brain - physiology
Cerebral blood flow (CBF)
cerebral hemodynamics
Computer Simulation
Deconvolution
Delay estimation
dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI)
Hemodynamics
Hemodynamics - physiology
Humans
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Middle Aged
Models, Cardiovascular
Nonlinear Dynamics
Signal Processing, Computer-Assisted
Singular value decomposition
Stochastic Processes
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Summary:An accurate characterization of tissue residue function R ( t ) in bolus-tracking magnetic resonance imaging is of crucial importance to quantify cerebral hemodynamics. R ( t ) estimation requires to solve a deconvolution problem. The most popular deconvolution method is singular value decomposition (SVD). However, SVD is known to bear some limitations, e.g., R ( t ) profiles exhibit nonphysiological oscillations and take on negative values. In addition, SVD estimates are biased in presence of bolus delay and dispersion. Recently, other deconvolution methods have been proposed, in particular block-circulant SVD (cSVD) and Tikhonov regularization (TIKH). Here we propose a new method based on nonlinear stochastic regularization (NSR). NSR is tested on simulated data and compared with SVD, cSVD, and TIKH in presence and absence of bolus dispersion. A clinical case in one patient has also been considered. NSR is shown to perform better than SVD, cSVD, and TIKH in reconstructing both the peak and the residue function, in particular when bolus dispersion is considered. In addition, differently from SVD, cSVD, and TIKH, NSR always provides positive and smooth R ( t ).
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2009.2013820