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Magnetic Resonance Imaging
Published in Jiří Jan, Medical Image Processing, Reconstruction and Analysis, 2019
Registration is also needed between the resulting fMRI activity-image volumes and the detailed anatomy depicting the MRI volume. As the imaging geometry (resolution, possible geometrical distortion, etc.) is different in both types of images, more advanced geometrical transforms must be used (i.e., the generic affine linear transform or even non-linear transforms may be needed). Because the contrast scales in both types of images are also quite different, this registration is of the multimodal type. The fit of volumes must be then evaluated by information-based similarity criteria, usually by some form of mutual information criterion. From the image-processing methodological point of view, a similar case is also the often-required spatial normalization of the resulting images (i.e., geometric transformation to a standardized size and space).
Working memory network plasticity after exercise intervention detected by task and resting-state functional MRI
Published in Journal of Sports Sciences, 2021
Lina Zhu, Xuan Xiong, Xiaoxiao Dong, Yi Zhao, Adam Kawczyński, Aiguo Chen, Wei Wang
We utilized DPARSFA advanced edition (DPARSFA 4.3, http://rfmri.org/DPARSF), which was based on SPM (http://www.fil.ion.ucl.ac.uk/spm) for the rs-fMRI data analysis. Preprocessing comprised the following steps: 1) removal of the first ten functional volumes; 2) slice-timing correction; 3) 3-dimensional motion correction; and 4) coregistration of individuals’ structural images to the functional images using a linear transformation, which were segmented into white matter, grey matter, and cerebrospinal fluid using a new segment algorithm in DARTEL; 5) spatial normalization using the MNI template and resampling to 3 × 3 × 3 mm voxels; 6) linear detrending and nuisance signal removal (white matter, cerebrospinal fluid, global signal, 6-head motion parameters, 6-head motion parameters at one time point earlier, and the 12 corresponding squared items (Friston 24-parameter model) as covariates) via multiple regressions; 7) spatial smoothing with a 6-mm full-width at half-maximum Gaussian kernel.; and 8) bandpass filtering (ranging from 0.01 to 0.01 Hz) to reduce the effects of low-frequency drift and high-frequency noise. In this study, we excluded four subjects according to the head motion criteria of a maximum spin (x, y, z) of <3.0° and a maximum cardinal direction displacement (x, y, z) of <3.0 mm.