Assessing the Streamline Plausibility Through Convex Optimization for Microstructure Informed Tractography(COMMIT) with Deep Learning

Abstract: Tractography is widely used in the brain connectivity study from diffusion magnetic resonance imaging data. However, lack of ground truth and plenty of anatomically implausible streamlines in the tractograms have caused challenges and concerns in the use of tractograms such as brain connectivity study. Tractogram filtering methods have been developed to remove the faulty connections. In this study, we focus on one of these filtering methods, Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT), which tries to find a set of streamlines that best reconstruct the diffusion magnetic resonance imaging data with global optimization approach. There are biases with this method when assessing individual streamlines. So a method named randomized COMMIT(rCOMMIT) is proposed to obtain multiple assessments for each streamline. The acceptance rate from this method is introduced to the streamlines and divides them into three groups, which are regarded as pseudo ground truth from rCOMMIT. Therefore, the neural networks are able to train on the pseudo ground truth on classification tasks. The trained classifiers distinguish the obtained groups of plausible and implausible streamlines with accuracy around 77%. Following the same methodology, the results from rCOMMIT and randomized SIFT are compared. The intersections between two methods are analyzed with neural networks as well, which achieve accuracy around 87% in binary task between plausible and implausible streamlines.