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2016
DOI: 10.1088/0266-5611/32/11/115009
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An explicit reconstruction algorithm for the transverse ray transform of a second rank tensor field from three axis data

Abstract: We give an explicit plane-by-plane filtered back-projection reconstruction algorithm for the transverse ray transform of symmetric second rank tensor fields on Euclidean 3-space, using data from rotation about three orthogonal axes. We show that in the general case two axis data is insufficient but give an explicit reconstruction procedure for the potential case with two axis data.

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Cited by 15 publications
(23 citation statements)
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References 12 publications
(28 reference statements)
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“…See [117] for a proof. To see how this relates to the Lens Rigidity Problem (see Problem 1), fix two metrics g andg on M and suppose they have same lens data 19 . Both metrics give rise to geodesic vector fields V and V on T * M , each of which uniquely characterizes g andg.…”
Section: From Problem 2 To Problemmentioning
confidence: 99%
See 2 more Smart Citations
“…See [117] for a proof. To see how this relates to the Lens Rigidity Problem (see Problem 1), fix two metrics g andg on M and suppose they have same lens data 19 . Both metrics give rise to geodesic vector fields V and V on T * M , each of which uniquely characterizes g andg.…”
Section: From Problem 2 To Problemmentioning
confidence: 99%
“…Nowadays this field forms the basis of several non-invasive approaches to imaging internal properties of materials: seismology [42,123], or how to reconstruct the density inside the Earth from first arrival times of seismic wavefronts; medical imaging since the development of X-ray Computerized Tomography [75, 119,25]; Single-Photon Emission Computerized Tomography using the attenuated X-ray transform [76,74,78]; vector tomography in helio-seismology [51,94,52]; ocean imaging [73]; X-ray diffraction strain tomography [59,19] and tomography in elastic media [103,Ch. 7][108]; neutron imaging, as applied to the imaging of vertebrate remains [102] and shales [11].…”
Section: Introductionmentioning
confidence: 99%
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“…This decomposition, Lemma 5.4, is very powerful for understanding the tensor tomography problem from an analytical stand-point. Theorem 5.3 lifts invertibility results from [30,47] to demonstrates when (or how much) the non-symmetric TRT is invertible. On the other hand, results from [47] also compute the (pseudo) inverses of the LRT and symmetric TRT which could be lifted to a filtered-back projection-like pseudo-inverse of the non-symmetric TRT.…”
Section: Invertibility Of Tensor Ray Transformsmentioning
confidence: 86%
“…This work is extended in Desai and Lionheart [4] through the presentation of an explicit inversion formula for the transverse ray transform. In general, it was shown that this formula allows for the reconstruction of strain from high-energy X-ray measurements made around three axes of rotation.…”
Section: Introductionmentioning
confidence: 99%