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The rapid evolution of neuroimaging techniques underscores the necessity for robust medical image registration algorithms, essential for the precise analysis of resting-state networks. This study introduces a comprehensive modular evaluation framework, designed to assess and compare the differences of four state-of-the-art algorithms in the field: FSL, ANTs, DARTEL, and AFNI. Our framework highlights the critical importance of algorithm selection in neuroimaging, addressing the unique challenges and strengths each algorithm presents in processing complex brain imaging data. Our rigorous evaluation delves into the algorithms’ differences, with a focus on spatial localisation accuracy and the fidelity of resting-state network identification. The comparative analysis uncovers distinct advantages and limitations inherent to each algorithm, illuminating how specific characteristics can shape neuroimaging study outcomes. For instance, we reveal FSL’s robustness in handling diverse datasets, ANTs’ precision in spatial normalisation, DARTEL’s suitability for large-scale studies, and AFNI’s adaptability in functional and structural image analysis. The findings highlight the nuanced considerations necessary in choosing the right registration algorithm for neuroimaging data, advocating for a bespoke approach based on the unique requirements of each study. This detailed analysis advances the field, guiding researchers towards more informed algorithm selection and application, thus aiming to improve the accuracy and reliability of neuroimaging outcomes. Presenting a clear, comprehensive overview of each algorithm within our novel framework, the study addresses the needs of the neuroimaging community and paves the way for future advancements in medical image registration. |
