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000143905 1001_ $$00000-0002-6840-6205$$aNath, Vishwesh$$b0
000143905 245__ $$aTractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.
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000143905 520__ $$aFiber 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/mm2 with 20, 45, and 64 diffusion gradient directions per shell, respectively.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 2019.
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000143905 7001_ $$aSchilling, Kurt G$$b1
000143905 7001_ $$aParvathaneni, Prasanna$$b2
000143905 7001_ $$aHuo, Yuankai$$b3
000143905 7001_ $$aBlaber, Justin A$$b4
000143905 7001_ $$aHainline, Allison E$$b5
000143905 7001_ $$aBarakovic, Muhamed$$b6
000143905 7001_ $$aRomascano, David$$b7
000143905 7001_ $$aRafael-Patino, Jonathan$$b8
000143905 7001_ $$aFrigo, Matteo$$b9
000143905 7001_ $$aGirard, Gabriel$$b10
000143905 7001_ $$aThiran, Jean-Philippe$$b11
000143905 7001_ $$aDaducci, Alessandro$$b12
000143905 7001_ $$aRowe, Matt$$b13
000143905 7001_ $$aRodrigues, Paulo$$b14
000143905 7001_ $$aPrčkovska, Vesna$$b15
000143905 7001_ $$aAydogan, Dogu B$$b16
000143905 7001_ $$aSun, Wei$$b17
000143905 7001_ $$aShi, Yonggang$$b18
000143905 7001_ $$aParker, William A$$b19
000143905 7001_ $$aOuld Ismail, Abdol A$$b20
000143905 7001_ $$aVerma, Ragini$$b21
000143905 7001_ $$aCabeen, Ryan P$$b22
000143905 7001_ $$aToga, Arthur W$$b23
000143905 7001_ $$aNewton, Allen T$$b24
000143905 7001_ $$0P:(DE-He78)2b499e82770acce94ed7795a0b986354$$aWasserthal, Jakob$$b25$$udkfz
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000143905 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b27$$udkfz
000143905 7001_ $$aSavini, Giovanni$$b28
000143905 7001_ $$aPalesi, Fulvia$$b29
000143905 7001_ $$aKaden, Enrico$$b30
000143905 7001_ $$aWu, Ye$$b31
000143905 7001_ $$aHe, Jianzhong$$b32
000143905 7001_ $$aFeng, Yuanjing$$b33
000143905 7001_ $$aPaquette, Michael$$b34
000143905 7001_ $$aRheault, Francois$$b35
000143905 7001_ $$aSidhu, Jasmeen$$b36
000143905 7001_ $$aLebel, Catherine$$b37
000143905 7001_ $$aLeemans, Alexander$$b38
000143905 7001_ $$aDescoteaux, Maxime$$b39
000143905 7001_ $$aDyrby, Tim B$$b40
000143905 7001_ $$aKang, Hakmook$$b41
000143905 7001_ $$aLandman, Bennett A$$b42
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