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Robust dynamic susceptibility contrast MR perfusion using 4D nonlinear noise filters
Author(s) -
Kosior Jayme Cameron,
Kosior Robert Karl,
Frayne Richard
Publication year - 2007
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21219
Subject(s) - imaging phantom , voxel , noise (video) , cerebral blood flow , filter (signal processing) , mean squared error , computer science , perfusion , perfusion scanning , partial volume , gaussian filter , nuclear medicine , biomedical engineering , mathematics , artificial intelligence , medicine , computer vision , radiology , statistics , cardiology , image (mathematics)
Purpose To investigate if 4D (simultaneous space and time) nonlinear filtering techniques can produce more robust cerebral blood flow (CBF) estimates by reducing noise in acquired dynamic susceptibility contrast (DSC) MR perfusion data. Materials and Methods A digital anthropomorphic brain perfusion phantom was constructed to analyze filter performance by: 1) deriving anthropomorphic tissue volume fractions from a human subject and 2) simulating DSC‐MR perfusion signals for voxels with mixed tissue for various signal‐to‐noise ratios (SNRs). DSC‐MR data for 11 acute ischemic stroke patients were also acquired at 3T. CBF maps cross‐calibrated so that normal white matter CBF was 22 mL/minute/100 g were produced from DSC‐MR data without filtering and from 4D‐Gaussian and 4D‐bilateral noise‐filtered DSC‐MR data. Results The nonlinear 4D‐bilateral filter yielded the lowest CBF root‐mean square error (RMSE) in the phantom experiments with noise (average RMSE across all tissues regions for no filtering, 4D‐Gaussian, and 4D‐bilateral was 5.3 mL/minute/100 g, 6.2 mL/minute/100 g, and 4.0 mL/minute/100 g, respectively) and had the best image quality in both the phantom and patient data. Conclusion Nonlinear 4D noise filters are better suited to the 4D nature of DSC‐MR data. Linear spatial filters are not appropriate and can produce larger CBF errors than without filtering. J. Magn. Reson. Imaging 2007. © 2007 Wiley‐Liss, Inc.