The Computational Anatomy Toolbox (CAT) is a comprehensive suite of tools for brain morphometric analyses, designed to be user-friendly and accessible to researchers at all levels. CAT offers a range of analysis options, including voxel-based, surface-based, and region-based morphometric analyses, and is compatible with both MATLAB and standalone versions. The software is integrated with SPM, allowing seamless integration with other neuroimaging tools. CAT includes multiple quality control options and covers the entire analysis workflow, from preprocessing to statistical analysis and visualization. Key features include longitudinal processing, quality control, mapping onto the cortical surface, and threshold-free cluster enhancement (TFCE). Evaluations have shown that CAT is accurate, sensitive, reliable, and robust, outperforming other common neuroimaging tools. An example application using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrates the effectiveness of CAT in detecting structural differences in Alzheimer's disease patients compared to controls. Overall, CAT is a valuable tool for both small and large datasets, offering ultra-fast processing and high sensitivity in detecting significant effects.The Computational Anatomy Toolbox (CAT) is a comprehensive suite of tools for brain morphometric analyses, designed to be user-friendly and accessible to researchers at all levels. CAT offers a range of analysis options, including voxel-based, surface-based, and region-based morphometric analyses, and is compatible with both MATLAB and standalone versions. The software is integrated with SPM, allowing seamless integration with other neuroimaging tools. CAT includes multiple quality control options and covers the entire analysis workflow, from preprocessing to statistical analysis and visualization. Key features include longitudinal processing, quality control, mapping onto the cortical surface, and threshold-free cluster enhancement (TFCE). Evaluations have shown that CAT is accurate, sensitive, reliable, and robust, outperforming other common neuroimaging tools. An example application using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrates the effectiveness of CAT in detecting structural differences in Alzheimer's disease patients compared to controls. Overall, CAT is a valuable tool for both small and large datasets, offering ultra-fast processing and high sensitivity in detecting significant effects.