IntroductionMagnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this study, 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.Methods and implementationAll functionalities of suMRak are implemented using the MATLAB App Designer and the MATLAB-integrated Python engine. A total of six 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.ResultsWhen compared with the 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 (p = 0.03) when compared to ITK-SNAP, 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 topographic connection to anatomical areas and enabling further data analysis to better account for the inherent spatial distortions of echoplanar imaging.DiscussionSuMRak 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.

中文翻译：

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suMRak：用于临床前脑 MRI 数据分析的多工具解决方案

简介磁共振成像（MRI）对于理解大脑疾病非常有价值，但数据的复杂性给实验研究带来了挑战。在本研究中，我们介绍了 suMRak，这是一款专为高效临床前脑部 MRI 分析而设计的 MATLAB 应用程序。 SuMRak 将大脑分割、体积分析、图像配准和参数图生成集成到一个统一的界面中，从而减少研究人员进行直接数据处理可能需要的单独工具的数量。方法和实现suMRak 的所有功能都是使用 MATLAB App Designer 和MATLAB 集成的 Python 引擎。与 suMRak 主界面一起开发了总共六个辅助应用程序，以实现连贯且简化的工作流程。通过将suMRak与手动分割和ITK-SNAP（一种流行的生物医学图像分割开源应用程序）进行比较，验证了大脑分割策略。结果与冠状小鼠大脑切片的手动分割相比，suMRak实现了较高的Sørensen-Dice相似系数（ 0.98±0.01)，接近手动精度。此外，suMRak 表现出显着的改进（p= 0.03）与 ITK-SNAP 相比，特别是对于位于尾部的脑切片。此外，suMRak 能够有效分析我们自己研究中获得的临床前 MRI 数据。最值得注意的是，显示了脑灌注图配准到 T2 加权图像的结果，改善了与解剖区域的地形连接，并实现了进一步的数据分析，以更好地解释回波平面成像的固有空间失真。DiscussionSuMRak 提供了临床前的高效 MRI 数据处理大脑图像，使研究人员能够保持一致性和准确性。值得注意的是，通过 K 均值聚类和形态学操作实现的加速大脑分割显着减少了处理时间，并可以更轻松地处理更大的数据集。