Hemodynamic changes are extremely important in analyzing responses from a brain subjected to a stimulus or treatment. The Laser Doppler technique has emerged as an important tool in neuroscience research. This non-invasive method scans a low-power laser beam in a raster pattern over a tissue surface to generate the time course of images in unit of relative flux changes. Laser Doppler imager (LDI) records cerebral perfusion not only in the temporal but also in the spatial domain. The traditional analysis of LD images has been focused on the region-of-interest (ROI) approach, in which the analytical accuracy in an experiment that necessitates a relative repositioning between the LDI and the scanned tissue area will be weakened due to the operator's subjective decision in data collecting. This report describes a robust image registration method designed to obviate this problem, which is based on the adaptive correlation approach. The assumption in mapping corresponding pixels in two images is to correlate the regions in which these pixels are centered. Based on this assumption, correlation coefficients are calculated between two regions by a method in which one region is moved around over the other in all possible combinations. To avoid ambiguity in distinguishing maximum correlation coefficients, an adaptive algorithm is adopted. Correspondences are then used to estimate the transformation by linear regression. We used a pair of phantom LD images to test this algorithm. A reliability test was also performed on each of the 15 sequential LD images derived from an actual experiment by imposing rotation and translation. The result shows that the calculated transformation parameters (rotation: θ̂=7.7±0.5°; translation: Δx̂=2.8±0.3, Δŷ=4.7±0.4) are very close to the prior-set parameters (rotation: θ=8°; translation: Δx=3, Δy=5). This result indicates that this approach is a valuable adjunct to LD perfusion monitoring. An original sequence of LD images that recorded cerebral perfusion through a cranial window before, during and after middle cerebral artery occlusion (MCAo) is presented, together with the registered image sequence. Cerebral perfusion data acquired in a pixel-based manner from different anatomic locations of the registered LD image sequence are also presented over the whole time-course of the experiment.
- Cerebral blood flow
- Laser Doppler image
- Middle cerebral artery occlusion
ASJC Scopus subject areas