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Neuroimage. 2019 Jan 15;185:1-11. doi: 10.1016/j.neuroimage.2018.10.029. Epub 2018 Oct 11.

Limits to anatomical accuracy of diffusion tractography using modern approaches.

Author information

1
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA. Electronic address: kurt.g.schilling.1@vumc.org.
2
Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, USA.
3
Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
4
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
5
Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
6
Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
7
Computer Science Department, University of Verona, Verona, Italy.
8
Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
9
Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
10
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
11
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
12
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
13
Cardiff University, Brain Research Imaging Centre, School of Psychology, Cardiff, UK.
14
Department of Radiology, Harvard Medical School, Boston, MA, USA.
15
Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
16
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
17
Brigham and Women's Hospital, Harvard Medical School, USA.
18
National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA.
19
Section on Learning and Plasticity, Laboratory of Brain and Cognition, NIMH, Bethesda, MD, USA.
20
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
21
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.

Abstract

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

KEYWORDS:

Connectivity; Diffusion MRI; Phantom; Tracer; Tractography; Validation; White matter

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