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Track-weighted imaging methods: extracting information from a streamlines tractogram

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Abstract

A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.

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Notes

  1. Throughout this work, the terms “streamline” and “track” are used interchangeably, to represent a mathematical representation (i.e. a three-dimensional curve generated using a tractography algorithm). In contrast, the terms “tract” and “white matter pathway” are also used interchangeably to represent the actual biological structure in the brain.

  2. The TOI is equivalent to the commonly used region of interest (ROI), for the particular case that its extent is determined by the volume occupied by a set of streamlines (typically corresponding to a given white matter structure).

  3. ACM and FDM are essentially similar to TDI at native resolution (i.e. without applying super-resolution).

  4. In the TDI analogy as a histogram map, super-resolution can be seen as the fact that the bin size (i.e. voxel size) can be, to some extent, arbitrarily chosen.

  5. The term “fixel” was introduced in that paper to refer to a specific fibre population within a single voxel.

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Acknowledgements

We are grateful to the many colleagues and collaborators involved in the track-weighted imaging work, and in particular to Alan Connelly, Jacques-Donald Tournier, and Robert E. Smith for their extensive contribution to developing these methods. We are also grateful to Chun-Hung Yeh and Donna Parker for help in producing figures for this work. We thank the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Grant for their support.

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  1. Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia

    Fernando Calamante

  2. Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia

    Fernando Calamante

  3. Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, VIC, Australia

    Fernando Calamante

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  1. Fernando Calamante
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Correspondence to Fernando Calamante.

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FC is co-inventor in a patent application on the TDI method.

Funding

This study was funded by the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Grant.

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Calamante, F. Track-weighted imaging methods: extracting information from a streamlines tractogram. Magn Reson Mater Phy 30, 317–335 (2017). https://doi.org/10.1007/s10334-017-0608-1

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