Reproducibility of diffusion-weighted structural connectomes is highly dependent on acquisition and tractography model, limiting the interpretation of connectomes acquired in the clinical setting. This study proposes a novel deep convolutional neural network (DCNN) to improve the reproducibility of structural connectomes, by which highly reproducible streamlines can be identified via an end-to-end deep learning of reference streamline coordinates in Human Connectome Project diffusion data. Preliminary results demonstrate that the proposed DCNN prediction model can improve the reproducibility of clinical connectomes (31.29% of F-statistics in intraclass correlation coefficient) and effectively remove noisy streamlines based on based on their poor prediction probabilities.
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