Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient-recalled echo-planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time-domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor-cortex and compared with more conventional image-subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross-correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.
Subject head movements are one of the main practical difficulties with brain functional MRI. A fast, accurate method for rotating and shifting a three-dimensional (3D) image using a shear factorization of the rotation matrix is described. Combined with gradient descent (repeated linearization) on a least squares objective function, 3D image realignment for small movements can be computed as rapidly as whole brain images can be acquired on current scanners. Magn Reson Med 42:
Functional magnetic resonance imaging (FMRI) is a new, noninvasive imaging tool thought to measure changes related to regional cerebral blood flow (rCBF). Previous FMRI studies have demonstrated functional changes within the primary cerebral cortex in response to simple activation tasks, but it is unknown whether FMRI can also detect changes within the nonprimary cortex in response to complex mental activities. We therefore scanned six right-handed healthy subjects while they performed self-paced simple and complex finger movements with the right and left hands. Some subjects also performed the tasks at a fixed rate (2 Hz) or imagined performing the complex task. Functional changes occurred (1) in the contralateral primary motor cortex during simple, self-paced movements; (2) in the contralateral (and occasionally ipsilateral) primary motor cortex, the supplementary motor area (SMA), the premotor cortex of both hemispheres, and the contralateral somatosensory cortex during complex, self-paced movements; (3) with less intensity during paced movements, presumably due to the slower movement rates associated with the paced (relative to self-paced) condition; and (4) in the SMA and, to a lesser degree, the premotor cortex during imagined complex movements. These preliminary results are consistent with hierarchical models of voluntary motor control.
In this study, Blood Oxygenation Level Dependent (BOLD) contrast in the detection of human brain activation was compared between spin-echo and gradient-echo echo-planar sequences at 1.5 T. Time course series of spin-echo and gradient-echo images containing the primary motor cortex were collected during rest (no finger movement) and activation (finger movement). Each time course series was collected using a different TE. Resting and active state signal intensities at each TE were measured in identical regions in the motor cortex. From these data, resting and active state R2 (1/T2) and R2* (1/T2*) values were obtained. Across four subjects, brain activation produced an average R2 change of -0.16 +/- 0.02/s (+/- SE), and an average R2* change of -0.55 +/- 0.08/s. The average delta R2*/delta R2 ratio was 3.52 +/- 0.56. The average gradient-echo/spin-echo ratio of activation-induced signal changes at the TE for maximal BOLD contrast for each sequence (TE approximately T2* and T2) was calculated to be 1.87 +/- 0.40.
A recursive algorithm suitable for functional magnetic resonance imaging (FMRI) calculations is presented. The correlation coefficient of a time course of images with a reference time series, with the mean and any linear trend projected out, may be computed with 22 operations per voxel, per image; the storage overhead is four numbers per voxel. A statistical model for the FMRI signal is presented, and thresholds for the correlation coefficient are derived from it. Selected images from the first real-time functional neuroimaging experiment (at 3 Tesla) are presented. Using a 50-MHz workstation equipped with a 14-bit analog-to-digital converter, each echo planar image was acquired, reconstructed, correlated, thresholded, and displayed in pseudocolor (highlighting active regions in the brain) within 500 ms of the RF pulse.
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