In functional brain images obtained by analyzing higher human brain functions using functional magnetic resonance imaging (fMRI), one serious problem is that these images depict false activation areas (artifacts) resulting from image-to-image physiological movements of subject during fMRI data acquisition. In order to truly detect functional activation areas, it is necessary to eliminate the effects of physiological movements of subject (i.e., gross head motion, pulsatile blood and cerebrospinal fluid (CSF) flow) from fMRI time series data. In this paper, we propose a method for eliminating artifacts due to not only rigid-body motion such as gross head motion, but also non-rigid-body motion like the deformation caused by the pulsatile blood and CSF flow. The proposed method estimates subject movements by using gradient methods which can detect subpixel optical flow. Our method estimates the subject movements on a "pixel-by-pixel" basis, and achieves the accurate estimation of both rigid-body and non-rigid-body motion. The artifacts are reduced by correction based on the estimated movements. Therefore, brain activation areas are accurately detected in functional brain images. We demonstrate that our method is valid by applying it to real fMRI data and that it can improve the detection of brain activation areas.

One serious problem affecting the rest and active state images obtained during a functional MRI (fMRI) study is that of involuntary subject movements inside the magnet while the imaging protocol is being carried out. The small signal intensity rise and small activation areas observed in the fMRI results, such as the statistical maps indicating the significance of the observed signal intensity difference between the rest and active states for each pixel, are greatly affected even by head displacements of less than one pixel. Near perfect alignment in the subpixel level of each image with respect to a reference, then, is necessary if the results are to be considered meaningful, specially in a clinical setting. In this paper we report the brain displacements that take place during a fMRI study with an image alignment method based on a refined crosscorrelation function which obtains fast (non-iterative) and precise values for the inplane rotation and X and Y translation correction factors. The performance of the method was tested with phantom experiments and fMRI studies using normal subjects executing a finger-tapping motor task. In all cases, subpixel translations and rotations were detected. The rest and active phases of the time course plots obtained from pixels in the primary motor area were well differentiated after only one pass of the motion correction program, giving enhanced activation zones. Other related areas such as the supplementary motor area became visible only after correction, and the number of pixels showing false activation was reduced.