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Tomography is widely used in medical imaging or industrial non-destructive testing applications. One costly and time consuming operation in any form of tomography is the process of data acquisition where a large number of measurements are made and collected data is used for image reconstruction. Data acquisition can slow down tomography to the point that the scanner cannot catch up with the speed of changes in the medium under test. By optimizing the information content of each measurement, we can reduce the number of measurements needed to achieve the target precision. Development of algorithms to optimize the information content of tomography measurements is the main goal of this article. Here, the dynamics of the medium and tomography measurements are formulated in the form of a Kalman estimation filter. A mathematical algorithm is developed to compute the optimal measurement matrix which minimizes the uncertainty left in the estimation of the distribution the tomography scanner is reconstructing. Results, as presented in the paper, show noticeable improvement is the quality of generated images when the medium is scanned by optimal measurements instead of traditional raster or random scanning protocols.
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Optimal data acquisition in tomography
In tomography, three-dimensional images of a medium are reconstructed from a set of two-dimensional projections. Each projection is the result of a measurement made by the scanner via radiating some form of energy and collecting the scattered field after interacting with the medium. The information content of these measurements is not equal, and one projection can be more informative than others. By choosing the most informative measurement at every step of scanning, an optimal tomography system can maximize the speed of data acquisition and temporal resolution of acquired images, reducing the operation cost and exposure to possible harmful radiations. The aim of this paper is to introduce mathematical algorithms that can be used to design measurements with optimal information content when imaging static or dynamically evolving objects.
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- Award ID(s):
- 2154267
- PAR ID:
- 10477319
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of the Optical Society of America A
- Volume:
- 40
- Issue:
- 12
- ISSN:
- 1084-7529; JOAOD6
- Format(s):
- Medium: X Size: Article No. 2259
- Size(s):
- Article No. 2259
- Sponsoring Org:
- National Science Foundation
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