Document Type : Conference Proceedings
Department of Medical Physics and Biomedical engineering, Tehran University of Medical Sciences, Tehran, Iran. Neuro-Imaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran.
Neuro-Imaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran.
McConnel Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada
Introduction: The purpose of multimodal and co-registration In MR Neuroimaging is to fuse two or more sets images (T1, T2, fMRI, DTI, pMRI, …) for combining the different information into a composite correlated data set in order to visualization, re-alignment and generating transform to functional Matrix. Multimodal registration and motion correction in spinal cord MR Neuroimaging data, often performed using affine and rigid transformations constrained in the axial or sagittal plane. However, since spinal cord geometry is articulated and respiration is a cause of slice-wise related shifts along the phase-encoding direction, non- rigid registration has been proposed. We tested a number of options for registration of multi- parameter spinal cord data (functional EPI/FSE data and Anatomic T2 SPACE). A result of the registration process is used to fuse information of MRI and fMRI and our technique that based on an efficient non-parametric image registration could be done in a semi-automated fashion, and alignment to the template cord performed using.
Materials and Methods: For a single subject, the functional MRI data set (EPI/T2*, FSE/T2) and diffusion (EPI) have been registered with anatomic images and standard space by multi- modal non-parametric registration algorithm. This algorithm is performed with different cost function (mutual information, normalized correlation, and least-square) and slice regularization along with cord. Registration results are compared two kinds of similarity measures; mutual information (MI) and correlation ratio (CR). To evaluate the performance of non-parametric technique, anatomical regions (Cervical and Lumbar spine) and image qualities, data were acquired in 18 data-set include Axial EPI-fMRI (TE/TR: 125/1250 mS, Matrix: 128×128), Sagittal FSE-fMRI (TE/TR: 76/750 mS, Matrix: 256×256). We used random Gaussian noise for two FSE dataset for exam our technique.
Results: Numerical measurements of the different cost function and slice regularization method for the sagittal FSE-fMRI and Axial GRE-EPI-fMRI data have been shown average MI=796.2, CR=0.73 for non-parametric and MI=864, CR=0.81 for multi-modal registration with normalized correlation + slice regularization. It can be concluded that the MI cost function produces better results when the images have good quality and the normalized correlation measure is more suitable for noisy images.
Conclusion: The optimized co-registration that was used is based on the slice by slice regularization along with the spinal cord in the spatial domain. Two similarity measures; correlation ratio and mutual information were used and results were compared with the results of the different measures of non-parametric registration. It was shown that for human MR Neuroimaging data the multi-modal non-parametric registration with normalized correlation cost function + regularization produces better results in terms of accuracy and time compared to parameters in the co-registration for both similarity measures. Also, it was shown that for noisy Sagittal FSE/fMRI images better registration results are produced when normalized correlation is used as the similarity measure.