Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI.

Research output: Contribution to journalArticlepeer-review

15 Citations (SciVal)
132 Downloads (Pure)

Abstract

Dynamic susceptibility-contrast (DSC) MRI requires deconvolution to retrieve the tissue residue function R(t) and the cerebral blood flow (CBF). In this study, deconvolution of time-series data was performed by wavelet-transform-based denoising combined with the Fourier transform (FT). Traditional FT-based deconvolution of noisy data requires frequency-domain filtering, often leading to excessive smoothing of the recovered signal. In the present approach, only a low degree of regularisation was employed while the major noise reduction was accomplished by wavelet transformation of data and Wiener-like filtering in the wavelet space. After inverse wavelet transform, the estimate of CBF.R(t) was obtained. DSC-MRI signal-versus-time curves (signal-to-noise ratios 40 and 100) were simulated, corresponding to CBF values in the range 10-60 ml/(min 100 g). Three shapes of the tissue residue function were investigated. The technique was also applied to six volunteers. Simulations showed CBF estimates with acceptable accuracy and precision, as well as independence of any time shift between the arterial input function and the tissue concentration curve. The grey-matter to white-matter CBF ratio in volunteers was 2.4 +/- 0.2. The proposed wavelet/FT deconvolution is robust and can be implemented into existing perfusion software. CBF maps from healthy volunteers showed high quality.
Original languageEnglish
Pages (from-to)113-118
JournalMagma
Volume18
Issue number3
DOIs
Publication statusPublished - 2005

Subject classification (UKÄ)

  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • wavelets
  • noise
  • cerebral blood flow
  • imaging
  • perfusion
  • magnetic resonance
  • deconvolution
  • dynamic susceptibility contrast

Fingerprint

Dive into the research topics of 'Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI.'. Together they form a unique fingerprint.

Cite this