Uncertainty in the DTI Visualization Pipeline

Springer, page 125--148 - February 2021
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Diffusion-Weighted Magnetic Resonance Imaging (DWI) enables the in-vivo visualization of fibrous tissues such as white matter in the brain. Diffusion-Tensor Imaging (DTI) specifically models the DWI diffusion measurements as asecond order-tensor. The processing pipeline to visualize this data, from image acqui-sition to the final rendering, is rather complex. It involves a considerable amount ofmeasurements, parameters and model assumptions, all of which generate uncertain-ties in the final result which typically are not shown to the analyst in the visualization.In recent years, there has been a considerable amount of work on the visualizationof uncertainty in DWI, and specifically DTI. In this chapter, we primarily focus onDTI given its simplicity and applicability, however, several aspects presented arevalid for DWI as a whole. We explore the various sources of uncertainties involved,approaches for modeling those uncertainties, and, finally, we survey different strate-gies to visually represent them. We also look at several related methods of uncertaintyvisualization that have been applied outside DTI and discuss how these techniquescan be adopted to the DTI domain. We conclude our discussion with an overview ofpotential research directions.

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BibTex references

@InBook { SHV21,
  author       = "Siddiqui, Faizan and H\öllt, Thomas and Vilanova, Anna",
  title        = "Uncertainty in the DTI Visualization Pipeline",
  chapter      = "6",
  pages        = "125--148",
  month        = "February",
  year         = "2021",
  publisher    = "Springer",
  doi          = "10.1007/978-3-030-56215-1_6",
  url          = "http://graphics.tudelft.nl/Publications-new/2021/SHV21"
}

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» Thomas Höllt
» Anna Vilanova






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