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Current Challenges and Future Directions in Diffusion MRI: From Model- to Data- Driven Analysis

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Computational Diffusion MRI

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

Diffusion weighted MRI is a prominent non-invasive modality to probe in vivo tissue micro- and macro-structure and has been widely applied throughout neuro- and body imaging. The promise of micro-scale analyses has been in the creation of virtual biopsies that provide information in place of physical histology, while tractography and its related methods offer maps of the neuronal wiring through virtual dissection. While both approaches have had strong successes at the group level, specificity and sensitivity at the individual dataset/single subject level have been more elusive. Herein, we reflect on current challenges and potential future directions in the context of a futurist piece. As such, we go beyond the reasonably well-established science to offer hypotheses/postulates/challenges to encourage discussion and exploration. We postulate that there are transformative opportunities available if we complement our perspective of diffusion MRI as a signal that is explained by a tractable biophysical model with one in which data driven machine learning can inform us about detection, localization, and assessment of both normal and abnormal brain tissue in both local (voxels) and global connectivity. Towards this end, this manuscript describes challenges associated with achieving virtual biopsy (i.e., microstructural modeling) and virtual dissection (i.e., fiber tractography) and suggests opportunities to use data-driven techniques to improve modeling geometry, to learn features of the signal that may prove useful as biomarkers, and to harmonize signal, techniques, and datasets to improve tissue characterization.

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Acknowledgments

This work was supported by NIH R01EB017230, NIH R01NS058639, and NIH T32EB001628.

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Authors and Affiliations

  1. Vanderbilt University Medical Center, Nashville, TN, 37235, USA

    Kurt G. Schilling, Baxter Rogers, Adam W. Anderson & Bennett A. Landman

  2. Vanderbilt University, Nashville, TN, 37235, USA

    Adam W. Anderson & Bennett A. Landman

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  1. Kurt G. Schilling
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  2. Baxter Rogers
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  3. Adam W. Anderson
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  4. Bennett A. Landman
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Correspondence to Bennett A. Landman .

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Editors and Affiliations

  1. Centre for Medical Imaging and Centre for Medical Image Computing, University College London, London, UK

    Elisenda Bonet-Carne

  2. Centre for Medical Engineering, King’s College London, London, UK

    Jana Hutter

  3. Centre for Medical Image Computing, University College London, London, UK

    Marco Palombo

  4. Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Marco Pizzolato

  5. Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA

    Farshid Sepehrband

  6. Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA

    Fan Zhang

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Schilling, K.G., Rogers, B., Anderson, A.W., Landman, B.A. (2020). Current Challenges and Future Directions in Diffusion MRI: From Model- to Data- Driven Analysis. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_6

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