AUTHOR=Ister Rok , Sternak Marko , Škokić Siniša , Gajović Srećko 

TITLE=suMRak: a multi-tool solution for preclinical brain MRI data analysis

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 18 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1358917

DOI=10.3389/fninf.2024.1358917

ISSN=1662-5196

ABSTRACT=MRI is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this paper, we introduce suMRak, a MATLAB application designed for efficient preclinical brain MRI analysis. SuMRak integrates brain segmentation, volumetry, image registration, and parameter map generation into a unified interface, thereby reducing the number of separate tools that researchers may require for straightforward data handling.All functionalities of suMRak are implemented using the MATLAB App Designer and the MATLAB integrated Python engine. A total of 6 helper applications were developed alongside the main suMRak interface to allow for a cohesive and streamlined workflow. The brain segmentation strategy was validated by comparing suMRak against manual segmentation and ITK-SNAP, a popular open-source application for biomedical image segmentation.When compared to manual segmentation of coronal mouse brain slices, suMRak achieved a high Sørensen-Dice similarity coefficient (0.98±0.01), approaching manual accuracy. Additionally, suMRak exhibited significant improvement to ITK-SNAP (p = 0.03), particularly for caudally located brain slices. Furthermore, suMRak was capable of effectively analyzing preclinical MRI data obtained in our own studies. Most notably, the results of brain perfusion map registration to T2 weighted images were shown, improving the topographical connection to anatomical areas, and enabling further data analysis to better account for inherent spatial distortions in echoplanar imaging.SuMRak offers efficient MRI data processing of preclinical brain images, enabling researchers' consistency and precision. Notably, the accelerated brain segmentation, achieved through K-means clustering and morphological operations, significantly reduces processing time and allows for easier handling of larger datasets.