Histogram tomography

William R. B. Lionheart

doi:10.3934/mine.2020004

Mathematics in Engineering, 2020, 2(1): 55-74. doi: 10.3934/mine.2020004. Research article Special Issues

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Histogram tomography

William R. B. Lionheart

Department of Mathematics, University of Manchester, Manchester, UK

^†This contribution is part of the Special Issue: Inverse problems in imaging and engineering science
Guest Editors: Lauri Oksanen; Mikko Salo
Link: https://www.aimspress.com/newsinfo/1270.html

Received: , Accepted: , Published:

Special Issues: Inverse problems in imaging and engineering science

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In many tomographic imaging problems the data consist of integrals along lines or curves. Increasingly we encounter “rich tomography” problems where the quantity imaged is higher dimensional than a scalar per voxel, including vectors tensors and functions. The data can also be higher dimensional and in many cases consists of a one or two dimensional spectrum for each ray. In many such cases the data contain not just integrals along rays but the distribution of values along the ray. If this is discretized into bins we can think of this as a histogram. In this paper we introduce the concept of “histogram tomography”. For scalar problems with histogram data this holds the possibility of reconstruction with fewer rays. In vector and tensor problems it holds the promise of reconstruction of images that are in the null space of related integral transforms. For scalar histogram tomography problems we show how bins in the histogram correspond to reconstructing level sets of function, while moments of the distribution are the x-ray transform of powers of the unknown function. In the vector case we suggest a reconstruction procedure for potential components of the field. We demonstrate how the histogram longitudinal ray transform data can be extracted from Bragg edge neutron spectral data and hence, using moments, a non-linear system of partial differential equations derived for the strain tensor. In x-ray diffraction tomography of strain the transverse ray transform can be deduced from the diffraction pattern the full histogram transverse ray transform cannot. We give an explicit example of distributions of strain along a line that produce the same diffraction pattern, and characterize the null space of the relevant transform.

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Keywords: tomography; distribution; histogram; spectrum; diffraction; strain; Radon transform; tensor tomography; vector tomography; x-ray diffraction; Bragg edge; neutron diffraction; Doppler transform

Citation: William R. B. Lionheart. Histogram tomography. Mathematics in Engineering, 2020, 2(1): 55-74. doi: 10.3934/mine.2020004

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This article has been cited by:

1. Lauri Oksanen, Mikko Salo, Inverse problems in imaging and engineering science, Mathematics in Engineering, 2020, 2, 2, 287, 10.3934/mine.2020014

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