Non-parametric deconvolution using Bezier curves for quantification of cerebral perfusion in dynamic susceptibility contrast MRI.

ABSTRACT: Deconvolution is an ill-posed inverse problem that tends to yield non-physiological residue functions R(t) in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). In this study, the use of Bézier curves is proposed for obtaining physiologically reasonable residue functions in perfusion MRI. Cubic Bézier curves were employed, ensuring R(0) = 1, bounded-input, bounded-output stability and a non-negative monotonically decreasing solution, resulting in 5 parameters to be optimized. Bézier deconvolution (BzD), implemented in a Bayesian framework, was tested by simulation under realistic conditions, including effects of arterial delay and dispersion. BzD was also applied to DSC-MRI data from a healthy volunteer. Bézier deconvolution showed robustness to different underlying resi

SUBMITTER: Chakwizira A 

PROVIDER: S-EPMC9463354 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature