Back
Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.
Journal article

Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.

  • Nath V Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
  • Schilling KG Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  • Parvathaneni P Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  • Huo Y Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  • Blaber JA Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
  • Hainline AE Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.
  • Barakovic M Signal Processing Lab (LTS5), EPFL, Switzerland.
  • Romascano D Signal Processing Lab (LTS5), EPFL, Switzerland.
  • Rafael-Patino J Signal Processing Lab (LTS5), EPFL, Switzerland.
  • Frigo M Signal Processing Lab (LTS5), EPFL, Switzerland.
  • Girard G Signal Processing Lab (LTS5), EPFL, Switzerland.
  • Thiran JP Signal Processing Lab (LTS5), EPFL, Switzerland.
  • Daducci A Computer Science Department, University of Verona, Italy.
  • Rowe M Mint Labs Inc., Boston, Massachusetts, USA.
  • Rodrigues P Mint Labs Inc., Boston, Massachusetts, USA.
  • Prčkovska V Mint Labs Inc., Boston, Massachusetts, USA.
  • Aydogan DB Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA.
  • Sun W Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA.
  • Shi Y Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA.
  • Parker WA Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA.
  • Ould Ismail AA Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA.
  • Verma R Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA.
  • Cabeen RP Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA.
  • Toga AW Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA.
  • Newton AT Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Wasserthal J Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Neher P Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Maier-Hein K Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Savini G Department of Physics, University of Milan, Milan, Italy.
  • Palesi F Brain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, Italy.
  • Kaden E Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
  • Wu Y Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China.
  • He J Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China.
  • Feng Y Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China.
  • Paquette M Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.
  • Rheault F Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.
  • Sidhu J Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.
  • Lebel C Department of Radiology, University of Calgary, Canada.
  • Leemans A Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.
  • Descoteaux M Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.
  • Dyrby TB Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark.
  • Kang H Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.
  • Landman BA Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
Show more…
  • 2019-06-11
Published in:
  • Journal of magnetic resonance imaging : JMRI. - 2020
DW-MRI
HARDI
challenge
in vivo
reproducibility
tractography
English BACKGROUND
Fiber tracking with diffusion-weighted MRI 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.


PURPOSE
To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap.


STUDY TYPE
A systematic review of algorithms and tract reproducibility studies.


SUBJECTS
Single healthy volunteers.


FIELD STRENGTH/SEQUENCE
3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm2 with 20, 45, and 64 diffusion gradient directions per shell, respectively.


ASSESSMENT
Nine international groups submitted 46 tractography algorithm entries each consisting 16 tracts per scan. The algorithms were assessed using intraclass correlation (ICC) and the Dice similarity measure.


STATISTICAL TESTS
Containment analysis was performed to assess if the submitted algorithms had containment within tracts of larger volume submissions. This also serves the purpose to detect if spurious submissions had been made.


RESULTS
The top five submissions had high ICC and Dice >0.88. Reproducibility was high within the top five submissions when assessed across sessions or across scanners: 0.87-0.97. Containment analysis shows that the top five submissions are contained within larger volume submissions. From the total of 16 tracts as an outcome relatively the number of tracts with high, moderate, and low reproducibility were 8, 4, and 4.


DATA CONCLUSION
The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices. Data and challenge infrastructure remain available for continued analysis and provide a platform for comparison.


LEVEL OF EVIDENCE
5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249.
Language
  • English
Open access status
green
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/37664
Statistics
Document views: 49 File downloads: