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FASSt: Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography

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Computational Diffusion MRI (CDMRI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14328))

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Abstract

Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric Autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.

This work is supported by the National Institute of Health (NIH) under grants R01EB022744, RF1AG077578, RF1AG056573, RF1AG064584, R21AG064776, U19AG078109, and P41EB015922.

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Authors and Affiliations

  1. Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, 90033, USA

    Yuan Li, Xinyu Nie & Yonggang Shi

  2. Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA

    Yuan Li, Xinyu Nie & Yonggang Shi

  3. Department of Computer and Data Sciences, Case School of Engineering, Case Western Reserve University (CWRU), Cleveland, OH, 44106, USA

    Yao Fu

Authors
  1. Yuan Li
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  2. Xinyu Nie
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  3. Yao Fu
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  4. Yonggang Shi
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Corresponding author

Correspondence to Yonggang Shi .

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Editors and Affiliations

  1. University of Illinois at Chicago, Chicago, IL, USA

    Muge Karaman

  2. Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia

    Remika Mito

  3. University College London, London, UK

    Elizabeth Powell

  4. Université de Sherbrooke, Sherbrooke, QC, Canada

    Francois Rheault

  5. Microsoft Research Cambridge, Milton, UK

    Stefan Winzeck

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Li, Y., Nie, X., Fu, Y., Shi, Y. (2023). FASSt: Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography. In: Karaman, M., Mito, R., Powell, E., Rheault, F., Winzeck, S. (eds) Computational Diffusion MRI. CDMRI 2023. Lecture Notes in Computer Science, vol 14328. Springer, Cham. https://doi.org/10.1007/978-3-031-47292-3_12

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  • DOI: https://doi.org/10.1007/978-3-031-47292-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47291-6

  • Online ISBN: 978-3-031-47292-3

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