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


Journal

Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850

Informations de publication

Date de publication:
01 2020
Historique:
received: 07 01 2019
accepted: 06 05 2019
pubmed: 11 6 2019
medline: 14 4 2021
entrez: 11 6 2019
Statut: ppublish

Résumé

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. 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. A systematic review of algorithms and tract reproducibility studies. Single healthy volunteers. 3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm 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. 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. 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. 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. 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249.

Sections du résumé

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/mm
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.

Identifiants

pubmed: 31179595
doi: 10.1002/jmri.26794
pmc: PMC6900461
mid: NIHMS1029852
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

234-249

Subventions

Organisme : NCATS NIH HHS
ID : UL1 TR000445
Pays : United States
Organisme : NIBIB NIH HHS
ID : T32 EB001628
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB017230
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS096606
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR024975-01
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR024975
Pays : United States

Informations de copyright

© 2019 International Society for Magnetic Resonance in Medicine.

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Auteurs

Vishwesh Nath (V)

Computer Science, Vanderbilt University, Nashville, Tennessee, USA.

Kurt G Schilling (KG)

Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Prasanna Parvathaneni (P)

Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Yuankai Huo (Y)

Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Justin A Blaber (JA)

Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Allison E Hainline (AE)

Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.

Muhamed Barakovic (M)

Signal Processing Lab (LTS5), EPFL, Switzerland.

David Romascano (D)

Signal Processing Lab (LTS5), EPFL, Switzerland.

Jonathan Rafael-Patino (J)

Signal Processing Lab (LTS5), EPFL, Switzerland.

Matteo Frigo (M)

Signal Processing Lab (LTS5), EPFL, Switzerland.

Gabriel Girard (G)

Signal Processing Lab (LTS5), EPFL, Switzerland.

Jean-Philippe Thiran (JP)

Signal Processing Lab (LTS5), EPFL, Switzerland.

Alessandro Daducci (A)

Computer Science Department, University of Verona, Italy.

Matt Rowe (M)

Mint Labs Inc., Boston, Massachusetts, USA.

Paulo Rodrigues (P)

Mint Labs Inc., Boston, Massachusetts, USA.

Vesna Prčkovska (V)

Mint Labs Inc., Boston, Massachusetts, USA.

Dogu B Aydogan (DB)

Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA.

Wei Sun (W)

Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA.

Yonggang Shi (Y)

Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA.

William A Parker (WA)

Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA.

Abdol A Ould Ismail (AA)

Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA.

Ragini Verma (R)

Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA.

Ryan P Cabeen (RP)

Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA.

Arthur W Toga (AW)

Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA.

Allen T Newton (AT)

Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Jakob Wasserthal (J)

Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Peter Neher (P)

Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Klaus Maier-Hein (K)

Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Giovanni Savini (G)

Department of Physics, University of Milan, Milan, Italy.

Fulvia Palesi (F)

Brain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, Italy.

Enrico Kaden (E)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

Ye Wu (Y)

Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China.

Jianzhong He (J)

Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China.

Yuanjing Feng (Y)

Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China.

Michael Paquette (M)

Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.

Francois Rheault (F)

Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.

Jasmeen Sidhu (J)

Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.

Catherine Lebel (C)

Department of Radiology, University of Calgary, Canada.

Alexander Leemans (A)

Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.

Maxime Descoteaux (M)

Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.

Tim B Dyrby (TB)

Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark.

Hakmook Kang (H)

Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.

Bennett A Landman (BA)

Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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