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Analysis of perfusion MRI in stroke: To deconvolve, or not to deconvolve
Author(s) -
Meijs Midas,
Christensen Soren,
Lansberg Maarten G.,
Albers Gregory W.,
Calamante Fernando
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26024
Subject(s) - deconvolution , residual , singular value decomposition , sensitivity (control systems) , noise (video) , computer science , blind deconvolution , algorithm , artificial intelligence , image (mathematics) , electronic engineering , engineering
Purpose There is currently controversy regarding the benefits of deconvolution‐based parameters in stroke imaging, with studies suggesting a similar infarct prediction using summary parameters. We investigate here the performance of deconvolution‐based parameters and summary parameters for dynamic‐susceptibility contrast (DSC) MRI analysis, with particular emphasis on precision. Methods Numerical simulations were used to assess the contribution of noise and arterial input function (AIF) variability to measurement precision. A realistic AIF range was defined based on in vivo data from an acute stroke clinical study. The simulated tissue curves were analyzed using two popular singular value decomposition (SVD) based algorithms, as well as using summary parameters. Results SVD‐based deconvolution methods were found to considerably reduce the AIF‐dependency, but a residual AIF bias remained on the calculated parameters. Summary parameters, in turn, show a lower sensitivity to noise. The residual AIF‐dependency for deconvolution methods and the large AIF‐sensitivity of summary parameters was greatly reduced when normalizing them relative to normal tissue. Conclusion Consistent with recent studies suggesting high performance of summary parameters in infarct prediction, our results suggest that DSC‐MRI analysis using properly normalized summary parameters may have advantages in terms of lower noise and AIF‐sensitivity as compared to commonly used deconvolution methods. Magn Reson Med 76:1282–1290, 2016. © 2015 Wiley Periodicals, Inc.