# A three-step, “brute-force” approach toward optimized affine spatial normalization

## TL;DR

This paper proposes a three-step method to improve the spatial normalization of MR images, especially for children and images with imperfections.

## Contribution

The study introduces a brute-force parameter exploration and preprocessing steps to enhance affine normalization in diverse datasets.

## Key findings

- Initial inhomogeneity correction improved affine fit, especially in images with high inhomogeneity.
- Using a complexity-reduced image improved normalization, particularly in younger children.
- Blindly exploring a wide parameter space improved fit for most subjects, especially infants and young children.

## Abstract

The first step in spatial normalization of magnetic resonance (MR) images commonly is an affine transformation, which may be vulnerable to image imperfections (such as inhomogeneities or “unusual” heads). Additionally, common software solutions use internal starting estimates to allow for a more efficient computation, which may pose a problem in datasets not conforming to these assumptions (such as those from children). In this technical note, three main questions were addressed: one, does the affine spatial normalization step implemented in SPM12 benefit from an initial inhomogeneity correction. Two, does using a complexity-reduced image version improve robustness when matching “unusual” images. And three, can a blind “brute-force” application of a wide range of parameter combinations improve the affine fit for unusual datasets in particular. A large database of 2081 image datasets was used, covering the full age range from birth to old age. All analyses were performed in Matlab. Results demonstrate that an initial removal of image inhomogeneities improved the affine fit particularly when more inhomogeneity was present. Further, using a complexity-reduced input image also improved the affine fit and was beneficial in younger children in particular. Finally, blindly exploring a very wide parameter space resulted in a better fit for the vast majority of subjects, but again particularly so in infants and young children. In summary, the suggested modifications were shown to improve the affine transformation in the large majority of datasets in general, and in children in particular. The changes can easily be implemented into SPM12.