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Improving Multi-Tensor Fitting with Global Information from Track Orientation Density Imaging

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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

The number of fascicles per-voxel N is a crucial parameter for multiple-fiber reconstruction methods from diffusion magnetic resonance imaging (dMRI) data, such as the multi-tensor model (MTM). This parameter is especially important when the goal is to provide bundle-specific tissue metrics. However, for MTM, statistical selection methods, such as the F-test, the Akaike and the Bayesian information criteria, tend to overestimate the number of tensors per-voxel for typical multi-shell dMRI acquisitions. In this work, we explore the reasons for this overestimation and propose to combine track orientation density imaging (TODI) with a robust MTM fitting framework to improve the estimation of N per-voxel. We conducted experiments on both synthetic and clinical in vivo dMRI data to validate our method, and we observed that tractography seems to be a useful spatial regularizer for N. Our results demonstrate the effectiveness of incorporating TODI for a more accurate and robust estimation of the intra-voxel number of fascicles.

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Author information

Authors and Affiliations

  1. University of Sherbrooke, Sherbrooke, QC, J1K2R1, Canada

    Erick Hernandez-Gutierrez & Maxime Descoteaux

  2. New York University School of Medicine, New York, NY, 10016, USA

    Ricardo Coronado-Leija

  3. Center of Research in Mathematics, 36023, Guanajuato, GT, Mexico

    Alonso Ramirez-Manzanares

  4. Imeka Solutions Inc., Sherbrooke, QC, J1H 4A7, Canada

    Maxime Descoteaux

  5. Pharma Research and Early Development, Neuroscience and Rare Diseases Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070, Basel, CH, Switzerland

    Muhamed Barakovic & Stefano Magon

Authors
  1. Erick Hernandez-Gutierrez
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  2. Ricardo Coronado-Leija
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  3. Alonso Ramirez-Manzanares
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  4. Muhamed Barakovic
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  5. Stefano Magon
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  6. Maxime Descoteaux
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Corresponding author

Correspondence to Erick Hernandez-Gutierrez .

Editor information

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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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Hernandez-Gutierrez, E., Coronado-Leija, R., Ramirez-Manzanares, A., Barakovic, M., Magon, S., Descoteaux, M. (2023). Improving Multi-Tensor Fitting with Global Information from Track Orientation Density Imaging. 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_4

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  • DOI: https://doi.org/10.1007/978-3-031-47292-3_4

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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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