Abstract

Currently, there are many methods for processing diffusion MRI (dMRI) tractography data, with the aim to identify the main white matter connections. However, methods like fiber clustering lack ground truth, making the evaluation of the effectiveness of different clustering algorithms problematic. An alternative to evaluate the performance and test the efficacy of these algorithms is to use simulated fiber datasets. Nevertheless, the simulation of this data is not trivial due to brain fibers' irregular and complex shape. Although many fiber bundle simulators exist, they have been developed for other purposes, such as validating tractography algorithms or local diffusion models. In addition, these simulators usually use simple fiber bundle configurations without considering complex bundle shapes. With this in mind, the main goal of this work is to implement a simulator of brain fiber bundles based on exponential curves for validating fiber clustering methods. This representation uses bundle centroids and shape parameters to obtain a more realistic appearance of the fascicles. The simulator was validated using a deep white matter fiber bundle atlas, obtaining a good percentage of intersection between the original and simulated bundles, of up to 82%. Furthermore, we used groups of simulated bundles for the whole brain to evaluate the performance of a fiber clustering algorithm (QuickBundles) when using different distance thresholds, showing the utility of the proposed simulator.