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Spatially filtering functional magnetic resonance imaging data
Author(s) -
Lowe Mark J.,
Sorenson James A.
Publication year - 1997
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.1910370514
Subject(s) - filter (signal processing) , signal (programming language) , noise (video) , computer science , kernel (algebra) , artificial intelligence , convolution (computer science) , gaussian , parametric statistics , spatial filter , spatial frequency , pattern recognition (psychology) , computer vision , image (mathematics) , physics , mathematics , optics , statistics , artificial neural network , combinatorics , quantum mechanics , programming language
When constructing MR images from acquired spatial frequency data, it can be beneficial to apply a low‐pass filter to remove high frequency noise from the resulting images. This amounts to attenuating high spatial frequency fluctuations that can affect detected MR signal. A study is presented of spatially filtering MR data and possible ramifications on detecting regionally specific activation signal. It is shown that absolute activation levels are strongly dependent on the parameters of the filter used in image construction and that significance of an activation signal can be enhanced through appropriate filter selection. A comparison is made between spatially filtering MR image data and applying a Gaussian convolution kernel to statistical parametric maps.