Stable spline deconvolution for dynamic susceptibility contrast MRI

Denis Peruzzo, Marco Castellaro, Gianluigi Pillonetto, Alessandra Bertoldo

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI. Methods: The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets. Results: The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD. Conclusion: The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801–1811, 2017.

Original languageEnglish
Pages (from-to)1801-1811
Number of pages11
JournalMagnetic Resonance in Medicine
Volume78
Issue number5
DOIs
Publication statusPublished - Nov 1 2017

Keywords

  • cerebral blood flow
  • deconvolution
  • magnetic resonance imaging
  • perfusion

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Stable spline deconvolution for dynamic susceptibility contrast MRI'. Together they form a unique fingerprint.

Cite this