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

In this study, the use of Bézier curves is proposed for obtaining physiologically reasonable residue functions in perfusion MRI.Materials and methodsCubic B ézier curves were employed, ensuringR(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 dela y and dispersion. BzD was also applied to DSC-MRI data from a healthy volunteer.ResultsB ézier deconvolution showed robustness to different underlying residue function shapes. Accurate perfusion estimates were observed, except for boxcar residue functions at low signal-to-noise ratio. BzD involving corrections for delay, dispersion, and delay with dispersion generally returned accurate results, except for some degree of cerebral blood flow (CBF) overestimation at low levels of each effect. Maps of mean transit time and delay were markedly different between BzD and block-circulant singular value decomposition (oSVD) deconvolution.DiscussionA novel DSC-MRI deconvolution method based on B ézier curves was implemented and evaluated. BzD produced physiologically plausible impulse response, without spurious oscillations, with generally less CBF underestimation than oSVD.

Source: Magnetic Resonance Materials in Physics, Biology and Medicine - January 13, 2022 Category: Materials Science Source Type: research