Trading Streamlines in Tractography using Autoencoders (TINTA)

Current tractography methods have a limited ability to accurately reconstruct the long-range brain white matter fiber pathways. Local orientation propagation methods provide tractograms with a non-negligible amount of implausible streamlines. In this work, we propose an artificial intelligence model to recover long-range white matter tracks that are potentially missed in conventional streamline propagation. Our method uses the generative ability of an autoencoder to propose new, plausible streamlines that are subsequently exchanged, according to a given similarity index, with the implausible streamlines in a given tractogram. This allows to potentially improve the reliability of the reconstructed fiber pathways.