<title>Grey-value-based 3D registration of functional MRI time-series: comparison of interpolation order and similarity measure</title>

Abstract

The analysis of functional MR images of the brain such as FMRI and neuro perfusion is significantly limited by movement of the head during image acquisition. Already small motions introduce artifacts in voxel-based statistical analysis and restrict the assessment of functional information. The retrospective compensation of head motion is usually addressed by image registration techniques which spatially align the images of the time-series. In this paper we investigate the relevance of intermediate interpolation during the registration process, similarity measure and optimization scheme by means of statistical consistency of the registration results. Experiments show that cubic and quartic interpolation remarkably improve the consistency when compared to linear methods. The use of larger interpolation kernels, however, does not result in further improvements. Measures based on the mean squared error are successfully applied to FMRI time-series which provide constant tissue-to-image transfer. However, they are not suitable for neuro perfusion imaging since the change of image intensity during the inflow of the contrast agent affords measures typically applied in multi-modality registration. Our results indicate that a recently proposed measure based on local correlation is preferable to mutual information in the case of neuro perfusion.

Motion correction in diffusion‐weighted MRI of the breast at 3T

“…The LC value is calculated by defining a neighborhood surrounding each voxel in the reference image ( b = 0 s/mm 2 ), calculating the correlation coefficient between the reference neighborhood and the corresponding neighborhood in the image being transformed ( b > 0 s/mm 2 ) and summing these values over the image. It has been shown that only voxels with high intensity variance within their local neighborhood contribute significantly to the LC calculation and use of this fraction of the total image voxels in the optimization process reduces processing time and produces a reliable result (26). The number of voxels selected for LC calculation with respect to the total number of image voxels is referred to as the LC fraction , and typically ranges from 5%–20%.…”

Quantitative Evaluation of Image-Based Distortion Correction in Diffusion Tensor Imaging

“…Consistency testing for the quantitative evaluation of registration results has already been used for matching serial brain images [24] and for time-series registration in functional MR imaging [25]. The registration consistency of a series of images is calculated as follows: given the images and , two cyclic registrations are performed:…”

Motion artifacts in standard clinical setting obscure disease-specific differences in quantitative susceptibility mapping

“…It optimized the six degrees of freedom of a rigid transformation by gradient descent with respect to normalized mutual information, a measure describing the entropy of the joint intensity histogram (Viola and Wells 1997). The software was successfully applied to other registration tasks recently (Netsch et al 2000, Wenzel et al 2010.…”