# MRI and CT Fusion in Stereotactic Electroencephalography: A Literature Review
## 4 Discussion  
This study provided a literature review about multi-modal image fusion, using articles between January 2010 and April 2021. The main techniques in image fusion and registration were described, and an overview of the main validation metrics and tools used for image fusion was given. Regarding the first research question, \"What are the existing methods of brain image fusion using CT and MRI?\", the procedure was divided into two steps: registration and merging. This review found developments in the use of registration to localize electrodes in SEEG. However, the lack of validation data with external objects was found to be a common problem [17, 18, 20, 21]. This problem led to the use of manual registration as a gold-standard for validation, which produced unclear results for the possible subjective errors. The use of datasets such as the RIRE or NIREP for validation can also lead to unclear results due to the lack of external objects in the images. With respect to the image merging techniques, there were methods related to machine learning algorithms. However, there was a lack of developments specific to problems that require external objects in multi-modal imaging. Concerning the second research question, \"What are the tools used to fuse multi-modal brain images?\", twenty-two tools were found for the registration and fusion of medical images, and from these, sixteen were open source. The most-used platforms for the development of these tools were ITK and MATLAB, which were used by seven of the found tools (Table 2). As regards the third research question, \"What are the metrics used to validate and compare image fusion methods?\", the two main validation methods were: using targets/fiducial points and using specific quantitative metrics. The first one measures the \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Tools** & **Open-Source** & **Operating System** & **Language** & **Software Platform** \\ \hline Drop & no & Linux, Windows & C++, Java & N/A \\ \hline Elastix & yes & Linux, macOS, Windows & C++, Python & ITK \\ \hline Flexible Algorithms for Image Registration & no & macOS, Windows & MATLAB & MATLAB \\ (FAIR) & no & Linux, macOS, Windows & C++ & N/A \\ \hline FMRIB Software Library (FSL) & no & Linux, macOS & Windows & C++ & FSL \\ \hline FMRIB’s Non-Linear Image Registration & no & Linux, macOS & C++ & N/A \\ Tool (FNIRT) & yes & Linux, macOS, Windows & C++ & N/A \\ \hline Hierarchical Attribute Matching Mechanism & no & Linux, Windows & C & N/A \\ for Elastic Registration (HAMMER) & yes & Linux, macOS, Windows & C++, Python & ITK \\ \hline Kroon & yes & macOS, Windows & MATLAB & MATLAB \\ \hline Medical Image Processing, Analysis, and & & & & \\ Visualization (MIPAV) & yes & Linux, macOS, Windows & Java & N/A \\ \hline Medical Image Registration Toolkit (MIRTK) & no & Linux, macOS, Windows & C & N/A \\ \hline Medical Imaging Toolkit (MITO) & yes & Windows & C++ & N/A \\ \hline NiftyReg & yes & Linux, macOS, Windows & C++ & N/A \\ \hline OsiriX & yes & macOS & C++ & N/A \\ \hline Plastimatch & yes & Linux, macOS, Windows & C++, C & N/A \\ \hline Statistical Parametric Mapping (SPM) & yes & macOS, Windows & MATLAB & MATLAB \\ \hline Symmetric Log-Domain Diffeomorphic & & & & \\ Image Registration (SLDIR) & yes & Linux, macOS, Windows & C++ & ITK \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._registration procedure using target points to compute the error between the registered moving image and a gold-standard. The necessity of target points causes difficulty in the error calculation, mainly in problems that lack public gold-standard databases such as SEEG. From the articles found (Table 2), the image merging methods were validated using metrics based on the resulting features and signal distortion. These metrics measure how the information is transferred to the fused images. In contrast, the registration validations measure the distortion in the resulting image, which commonly requires a gold-standard.