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Multi-modality Imaging with Structure-Promoting Regularizers

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Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Abstract

Imaging with multiple modalities or multiple channels is becoming increasingly important for our modern society. A key tool for understanding and early diagnosis of cancer and dementia is PET-MR, a combined positron emission tomography and magnetic resonance imaging scanner which can simultaneously acquire functional and anatomical data. Similarly, in remote sensing, while hyperspectral sensors may allow to characterize and distinguish materials, digital cameras offer high spatial resolution to delineate objects. In both of these examples, the imaging modalities can be considered individually or jointly. In this chapter we discuss mathematical approaches which allow combining information from several imaging modalities so that multi-modality imaging can be more than just the sum of its components.

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Acknowledgements

The author acknowledges support from the EPSRC grant EP/S026045/1 and the Faraday Institution EP/T007745/1. Moreover, the author is grateful to all his collaborators which indirectly contributed to this chapter over the last couple of years.

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Correspondence to Matthias J. Ehrhardt .

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Ehrhardt, M.J. (2021). Multi-modality Imaging with Structure-Promoting Regularizers. In: Chen, K., Schönlieb, CB., Tai, XC., Younces, L. (eds) Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer, Cham. https://doi.org/10.1007/978-3-030-03009-4_58-1

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  • DOI: https://doi.org/10.1007/978-3-030-03009-4_58-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03009-4

  • Online ISBN: 978-3-030-03009-4

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