Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation

Abstract

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual informationbased parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

“…While there are sophisticated tools to compute these registrations (Avants et al, 2009;Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012), the performance is limited by nontrivial factors. For example, the intermodality registration between dMRI and T1-weighted can be affected by differences in image resolutions (Malinsky et al, 2013) echo-planar imaging (EPI) distortion in dMRI data (Albi et al, 2018). Large individual anatomical variations with respect to the atlas population could also affect, or even cause to fail, the subject-specific T1 registration to the atlas.…”

Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract‐Specific Effects

“…In B0T2, the similarity of contrast between the two images allowed the use of the CC metric. For FA to T1 registration, the metric used in the original work was normalized CC, so we applied the most similar available metric (CC) provided by ANTs and also investigated MI. Pipeline mDWIT1 used MI for intermodality registration.…”

“…This technique has high potential for future implementation in neuronavigation systems, as it requires only MRI data that are standardly acquired for presurgical planning. The literature includes many different pipelines for image‐registration‐based EPI distortion correction . Nonetheless, none of the proposed pipelines is specifically addressed to neurosurgical planning, and despite the multitude of the options available, their implementation is not widespread for clinical use.…”

Simultaneous registration of structural and diffusion weighed images using the full DTI information

“…Chiang et al 9 have used information theory and Kullback-Leibler divergence between probability density functions for cost metric. Malinsky et al 2 have registered the fractional anisotropy together with the T1 images. Finally, the two-dimensional tensors may not even capture enough of the complexity of the fibers and it has been proposed by Barpoutis et al 10 to register fourth-order tensors as well.…”