Vishwesh Nath1, Kurt G Schilling2, Prasanna Parvathaneni3, Yuankai Huo3, Justin A Blaber3, Allison E Hainline4, Muhamed Barakovic5, David Romascano5, Jonathan Rafael-Patino5, Matteo Frigo5, Gabriel Girard5, Jean-Philippe Thiran5, Alessandro Daducci6, Matt Rowe7, Paulo Rodrigues7, Vesna Prčkovska7, Dogu B Aydogan8, Wei Sun8, Yonggang Shi8, William A Parker9, Abdol A Ould Ismail9, Ragini Verma9, Ryan P Cabeen10, Arthur W Toga10, Allen T Newton11, Jakob Wasserthal12, Peter Neher12, Klaus Maier-Hein12, Giovanni Savini13, Fulvia Palesi14, Enrico Kaden15, Ye Wu16, Jianzhong He16, Yuanjing Feng16, Michael Paquette17, Francois Rheault17, Jasmeen Sidhu17, Catherine Lebel18, Alexander Leemans19, Maxime Descoteaux17, Tim B Dyrby20, Hakmook Kang4, Bennett A Landman1,2,3,11. 1. Computer Science, Vanderbilt University, Nashville, Tennessee, USA. 2. Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 3. Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 4. Biostatistics, Vanderbilt University, Nashville, Tennessee, USA. 5. Signal Processing Lab (LTS5), EPFL, Switzerland. 6. Computer Science Department, University of Verona, Italy. 7. Mint Labs Inc., Boston, Massachusetts, USA. 8. Keck School of Medicine, University of Southern California (NICR), Los Angeles, California, USA. 9. Center for Biomedical Image Computing and Analytics, Dept. of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN), Philadelphia, Pennsylvania, USA. 10. Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA. 11. Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 12. Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. 13. Department of Physics, University of Milan, Milan, Italy. 14. Brain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, Italy. 15. Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK. 16. Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China. 17. Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada. 18. Department of Radiology, University of Calgary, Canada. 19. Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands. 20. Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark.
Abstract
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.
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.
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