Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms

K. Aditya Mohan

doi:10.1117/12.2275185

3 October 2017 Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms

K. Aditya Mohan

Proceedings Volume 10391, Developments in X-Ray Tomography XI; 103910U (2017) https://doi.org/10.1117/12.2275185
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

4D X-ray computed tomography (4D-XCT) is widely used to perform non-destructive characterization of time varying physical processes in various materials. The conventional approach to improving temporal resolution in 4D-XCT involves the development of expensive and complex instrumentation that acquire data faster with reduced noise. It is customary to acquire data with many tomographic views at a high signal to noise ratio. Instead, temporal resolution can be improved using regularized iterative algorithms that are less sensitive to noise and limited views. These algorithms benefit from optimization of other parameters such as the view sampling strategy while improving temporal resolution by reducing the total number of views or the detector exposure time. This paper presents the design principles of 4D-XCT experiments when using regularized iterative algorithms derived using the framework of model-based reconstruction. A strategy for performing 4D-XCT experiments is presented that allows for improving the temporal resolution by progressively reducing the number of views or the detector exposure time. Theoretical analysis of the effect of the data acquisition parameters on the detector signal to noise ratio, spatial reconstruction resolution, and temporal reconstruction resolution is also presented in this paper.

Conference Presentation

Citation Download Citation

K. Aditya Mohan "Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms", Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910U (3 October 2017); https://doi.org/10.1117/12.2275185

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