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Diffusion MRI microstructure models with in vivo human brain Connectome data : Results from a multi-group comparison

Ferizi, Uran (författare)
University College London (UCL), United Kingdom,University College London (UCL), United Kingdom
Scherrer, Benoit (författare)
Harvard University, USA
Schneider, Torben (författare)
University College London (UCL), United Kingdom
visa fler...
Alipoor, Mohammad (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Eufracio, Odin (författare)
Mathematics Research Center
Fick, Rutger H.J. (författare)
National Institute for Research in Computer Science and Control (INIRA)
Deriche, Rachid (författare)
National Institute for Research in Computer Science and Control (INIRA)
Nilsson, Markus (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Lund University Bioimaging Center,MR Physics,Forskargrupper vid Lunds universitet,Diagnostic Radiology, (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund University Research Groups
Loya-Olivas, Ana K. (författare)
Mathematics Research Center
Rivera, Mariano (författare)
Mathematics Research Center
Poot, Dirk H.J. (författare)
Delft University of Technology (TU Delft), Netherlands
Ramirez-Manzanares, Alonso (författare)
Marroquin, Jose L. (författare)
Rokem, Ariel (författare)
Stanford University, USA,University of Washington, USA
Pötter, Christian (författare)
Stanford University, USA
Dougherty, Robert F. (författare)
Stanford University, USA
Sakaie, Ken (författare)
Harvard University, USA
Wheeler-Kingshott, Claudia (författare)
University College London (UCL), United Kingdom
Warfield, Simon K. (författare)
Witzel, Thomas (författare)
Harvard University, USA
Wald, Lawrence L. (författare)
Harvard University, USA
Raya, José G. (författare)
Alexander, Daniel C (författare)
University College London (UCL), United Kingdom
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John Wiley & Sons Inc. 2017
Ingår i: NMR in Biomedicine. - : John Wiley & Sons Inc.. - 0952-3480 .- 1099-1492. ; 30:9
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
  • A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the 'White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.


MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)


Brain microstructure
Diffusion MRI
Model selection
diffusion MRI
brain microstructure
model selection

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