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Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

Vishwesh Nath, Kurt G. Schilling, Prasanna Parvathaneni, Allison E. Hainline, Yuankai Huo, Justin A. Blaber, Matt Rowe, Paulo Rodrigues, Vesna Prchkovska, Dogu Baran Aydogan, Wei Sun, Yonggang Shi, William A. Parker, Abdol Aziz Ould Ismail, Ragini Verma, Ryan P. Cabeen, Arthur W. Toga, Allen T. Newton, Jakob Wasserthal, Peter Neher, Klaus Maier-Hein, Giovanni Savini, Fulvia Palesi, Enrico Kaden, Ye Wu, Jianzhong He, Yuanjing Feng, Muhamed Barakovic, David Romascano, Jonathan Rafael-Patino, Matteo Frigo, Gabriel Girard, Alessandro Daducci, Jean-Philippe Thiran, Michael Paquette, Francois Rheault, Jasmeen Sidhu, Catherine Lebel, Alexander Leemans, Maxime Descoteaux, Tim B. Dyrby, Hakmook Kang, Bennett A. Landman
doi: https://doi.org/10.1101/484543
Vishwesh Nath
Computer Science, Vanderbilt University, Nashville, TN, USA
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Kurt G. Schilling
Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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Prasanna Parvathaneni
Electrical Engineering, Vanderbilt University, Nashville, TN, USA
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Allison E. Hainline
Biostatistics, Vanderbilt University, Nashville, TN, USA
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Yuankai Huo
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Justin A. Blaber
Electrical Engineering, Vanderbilt University, Nashville, TN, USA
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Matt Rowe
Mint Labs Inc., Boston, USA
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Paulo Rodrigues
Mint Labs Inc., Boston, USA
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Vesna Prchkovska
Mint Labs Inc., Boston, USA
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Dogu Baran Aydogan
Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
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Wei Sun
Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
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Yonggang Shi
Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
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William A. Parker
Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
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Abdol Aziz Ould Ismail
Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
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Ragini Verma
Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
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Ryan P. Cabeen
Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute
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Arthur W. Toga
Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute
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Allen T. Newton
Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USAVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
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Jakob Wasserthal
Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Peter Neher
Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Klaus Maier-Hein
Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Giovanni Savini
Department of Physics, University of Milan, Milan, ItalyBrain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, Italy
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Fulvia Palesi
Brain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, ItalyDepartment of Physics, University of Pavia Pavia, Italy
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Enrico Kaden
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Ye Wu
Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
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Jianzhong He
Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
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Yuanjing Feng
Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
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Muhamed Barakovic
Signal Processing Lab (LTS5), EPFL, Switzerland
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David Romascano
Signal Processing Lab (LTS5), EPFL, Switzerland
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Jonathan Rafael-Patino
Signal Processing Lab (LTS5), EPFL, Switzerland
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Matteo Frigo
Signal Processing Lab (LTS5), EPFL, Switzerland
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Gabriel Girard
Signal Processing Lab (LTS5), EPFL, Switzerland
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Alessandro Daducci
Computer Science Department, University of Verona, ItalyRadiology Department, CHUV and University of Lausanne, SwitzerlandSignal Processing Lab (LTS5), EPFL, Switzerland
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Jean-Philippe Thiran
Signal Processing Lab (LTS5), EPFL, SwitzerlandRadiology Department, CHUV and University of Lausanne, Switzerland
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Michael Paquette
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
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Francois Rheault
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
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Jasmeen Sidhu
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
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Catherine Lebel
Department of Radiology, University of Calgary, Canada
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Alexander Leemans
Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
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Maxime Descoteaux
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
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Tim B. Dyrby
Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark
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Hakmook Kang
Biostatistics, Vanderbilt University, Nashville, TN, USA
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Bennett A. Landman
Computer Science, Vanderbilt University, Nashville, TN, USABiomedical Engineering, Vanderbilt University, Nashville, TN, USAElectrical Engineering, Vanderbilt University, Nashville, TN, USARadiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USAVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
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ABSTRACT

Purpose Fiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do not capture variabilities because of in vivo physiological factors.

Methods To date, a large-scale reproducibility analysis has not been performed for the assessment of the newest generation of tractography algorithms with in vivo data. Reproducibility does not assess the validity of a brain connection however it is still of critical importance because it describes the variability for an algorithm in group studies. The ISMRM 2017 TraCED challenge was created to fulfill the gap. The TraCED dataset consists of a single healthy volunteer scanned on two different scanners of the same manufacturer. The multi-shell acquisition included b-values of 1000, 2000 and 3000 s/mm2 with 20, 45 and 64 diffusion gradient directions per shell, respectively.

Results Nine international groups submitted 46 tractography algorithm entries. The top five submissions had high ICC > 0.88. Reproducibility is high within these top 5 submissions when assessed across sessions or across scanners. However, it can be directly attributed to containment of smaller volume tracts in larger volume tracts. This holds true for the top five submissions where they are contained in a specific order. While most algorithms are contained in an ordering there are some outliers.

Conclusion The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices (i.e., volumetrically smaller tractograms). The data and challenge infrastructure remain available for continued analysis and provide a platform for comparison.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 03, 2018.
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Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge
Vishwesh Nath, Kurt G. Schilling, Prasanna Parvathaneni, Allison E. Hainline, Yuankai Huo, Justin A. Blaber, Matt Rowe, Paulo Rodrigues, Vesna Prchkovska, Dogu Baran Aydogan, Wei Sun, Yonggang Shi, William A. Parker, Abdol Aziz Ould Ismail, Ragini Verma, Ryan P. Cabeen, Arthur W. Toga, Allen T. Newton, Jakob Wasserthal, Peter Neher, Klaus Maier-Hein, Giovanni Savini, Fulvia Palesi, Enrico Kaden, Ye Wu, Jianzhong He, Yuanjing Feng, Muhamed Barakovic, David Romascano, Jonathan Rafael-Patino, Matteo Frigo, Gabriel Girard, Alessandro Daducci, Jean-Philippe Thiran, Michael Paquette, Francois Rheault, Jasmeen Sidhu, Catherine Lebel, Alexander Leemans, Maxime Descoteaux, Tim B. Dyrby, Hakmook Kang, Bennett A. Landman
bioRxiv 484543; doi: https://doi.org/10.1101/484543
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Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge
Vishwesh Nath, Kurt G. Schilling, Prasanna Parvathaneni, Allison E. Hainline, Yuankai Huo, Justin A. Blaber, Matt Rowe, Paulo Rodrigues, Vesna Prchkovska, Dogu Baran Aydogan, Wei Sun, Yonggang Shi, William A. Parker, Abdol Aziz Ould Ismail, Ragini Verma, Ryan P. Cabeen, Arthur W. Toga, Allen T. Newton, Jakob Wasserthal, Peter Neher, Klaus Maier-Hein, Giovanni Savini, Fulvia Palesi, Enrico Kaden, Ye Wu, Jianzhong He, Yuanjing Feng, Muhamed Barakovic, David Romascano, Jonathan Rafael-Patino, Matteo Frigo, Gabriel Girard, Alessandro Daducci, Jean-Philippe Thiran, Michael Paquette, Francois Rheault, Jasmeen Sidhu, Catherine Lebel, Alexander Leemans, Maxime Descoteaux, Tim B. Dyrby, Hakmook Kang, Bennett A. Landman
bioRxiv 484543; doi: https://doi.org/10.1101/484543

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