Neuroimaging Data Processing/Coregistration

Coregistration
Generally, coregistration refers to the spatial alignment of a series of images, either between two or among more image volumes. Methodologically, this overlaps with Realignment and Normalization. However, what is discussed in this section is the coregistration of functional and structural images.

If the aim is to study how functional activations of a subject overlay to individual's own anatomy, functional and structural images of the same brain should be aligned together. However, the difference between the functional and structural images from the same brain is not trivial. By contrast to the high-resolution structural images with clear region boundary contours, functional images are normally blurry and suffered from geometric and intensity distortions. The basic idea regarding coregistration herein is similar to the realignment, i.e. defining a cost function with the goal to minimize the differences on image parameters among images. However, because of distortions on functional images, the rigid-body transformation with six parameters may be not enough to correct. Depending on the complexity of distortions, either a nine-parameter transformation with another three additional parameters accounting for scaling differences on x-, y- or z- axes or even more sophisticated algorithms could be used to quantify the cost function. Meanwhile, as a result of the different contrasts between functional and structural images, the mutual information is more suitable to act as cost function than the sum of squared differences.

SPM
SPM provides COREGISTER module to implement a coregistration

AFNI
align_epi_anat.py script computes the alignment between two datasets, typically an EPI and an anatomical structural dataset, and applies the resulting transformation to one or the other to bring them into alignment. The transformation is calculated to align the anatomical to the epi data, but the resulting transformation can be used either way that is specified. Basic input is anatomical and epi dataset, which epi volume should be the base of alignment, direction of alignment (0/mean/median/max/volume#) and which direction of alignement is required (anat2epi/epi2anat), e.g.: align_epi_anat.py -anat ANATOMICALDATA -epi EPIDATA -epi_base 5

In afni_proc.py the align block is not set by default but can be included by do_block align. By default this means anat2epi registration, which can be changed with the following option: -volreg_align_e2a