# MRI and CT Fusion in Stereotactic Electroencephalography: A Literature Review
## 3 Results
### Methods of Image Fusion
#### 3.1.1 Image Registration
The accuracy was the error between the electrodes from the photography and the electrodes from the 3D MRI. While the method yielded a low average localization error of 1.31 \(\pm\) 0.69 mm for 385 electrodes, the accuracy measurement procedure did not consider the error of the projection, which implies uncertainty if the error comes from the registration or the projection procedure. [PERSON] et al. [18] used non-rigid transformation with MRI and CT to evaluate if the registration improved the localization of the SEEG electrodes. For the registration, [PERSON] used BrainLab iPlan, which is a radiosurgery planning software for rigid registration, and for non-rigid registration, the Automated Elastic image Fusion algorithm (AEF) and the Guided Elastic image Fusion algorithm (GEF) were used. The non-rigid registration improved the fusion, but there were unclear results in the localization of the electrodes, caused mainly by the evaluation method, which measured the localization performance using the distance between the electrodes and the brain cortex and not the real position of \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Paper Transform** & **Optimization Metric** & **Optimization Method** \\ \hline
[15] & Rigid &
\begin{tabular}{l} Mutual information, with curvelet- \\ based sampling \\ \end{tabular} & Gradient descent \\ \hline
[16] & Rigid &
\begin{tabular}{l} Interaction energy and the mutual information \\ \end{tabular} & Genetic Algorithm (GA) \\ \hline
[17] & Rigid & Mutual information & Powell’s method \\ \hline
[18] & Non-rigid & N/A & N/A \\ \hline
[19] & Rigid & Normalized Gradient Fields (NGFs) & Multilevel Gauss–Newton approach \\ \hline
[20] & Rigid & Normalized Mutual Information (NMI) & Gradient descent \\ \hline
[21] & Rigid & Mutual information & Gradient descent \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Articles about multi-modal image registration methods. the electrodes in the brain. Another cause for the unclear results was the lack of images for the validation, which was present in other studies that used intracranial electrodes, such as the method proposed by [PERSON] et al. [21] or the method proposed by [PERSON] et al. [20], which used the MI and the NMI, respectively, for the registration. The MI and the NMI also have some drawbacks due to Shannon's entropy, which assumes voxel value independence, but images lack this characteristic. However, a change in the sampling reduces this problem by selecting the less dependent voxel. With this premise, [PERSON] et al. [15] in 2011 proposed a sampling method using the curvelet transform, which is an extension of the wavelet transform, more suitable for two-dimensional and three-dimensional signals. [PERSON] et al. used a sampling method with a rigid registration, using the Retrospective Image Registration Evaluation database (RIRE), a public database used to validate registration methods. The method yields better results in the Target Registration Error (TRE) compared to methods based on uniform and gradient-based sampling. Some authors have used metrics different from the previous ones, such as the multi-modal registration procedure proposed by [PERSON] et al. [16]. [PERSON] et al. 's method uses Evolutionary Rigid-Body Docking (ERBD) algorithms, which is a docking technique that predicts the optimal configuration between two molecules. This registration takes the input images as molecules and minimizes the energy between the images using evolutionary algorithms. The ERBD composes the energy from two metrics: the interaction energy and the MI. [PERSON] et al. developed this method using MATLAB, and it only works with 2D images, causing difficulties in the validation for the use of only a few slices of a patient from the RIRE dataset. For clinical use, this method requires a further development and validation for 3D images. Another example of a registration procedure using a metric different from the MI is the method proposed by [PERSON] et al. [19].