Dr. Andrew M. Kingston Profile

Dr. Andrew M. Kingston

at Australian National Univ

SPIE Involvement:

Author

Publications (20)

SPIE Journal Paper | 6 May 2022

Least-squares and maximum-likelihood in computed tomography

Murdock Grewar, Glenn Myers, Andrew Kingston

JMI, Vol. 9, Issue 03, 031508, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.3.031508

KEYWORDS: Reconstruction algorithms, Data modeling, Photons, Computed tomography, X-rays, Signal attenuation, Mathematical modeling, Expectation maximization algorithms, Computer simulations, Statistical modeling

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Purpose: Existing maximum-likelihood (ML) methods in computed tomography usually require significant computing resources to implement, and/or are limited to particular measurement noise models that are representative of the simplest theoretical archetypes. There is an absence of general procedures to produce rapid ML methods that account precisely for the noise model of a given experiment. We investigate a mathematical-computational procedure of producing constrained quadratic optimization reconstruction algorithms that fill this niche, requiring less computing resources than the exact (expectation-maximization) procedures and having comparable performance with least-squares iterative methods. This allows high-fidelity reconstructions to be practically achievable for largely arbitrary noise models. Approach: We identify a systematic mathematical procedure to produce constrained quadratic optimization methods that maximize tomogram likelihood under arbitrary noise models, which are tunable to specific characteristics of the experiment. This procedure is applied to a general theory of mixed Poisson–Gaussian noise in transmission tomography, and to a theory of invertible linear transformations of measurement intensity subject to Poisson noise. We perform tomographic reconstructions of a very highly attenuating two-dimensional object phantom and compare the speed and fidelity of reconstruction with alternative quadratic metrics (ℓ2—minimization among others). Results: Quantitative metrics reveal that reconstructions under our systematically produced quadratic methods achieved significantly greater reconstruction fidelity with less computation than the optimized conventional, untuned quadratic metrics with a comparable procedure. Conclusion: Constrained quadratic optimization methods appear to apply sufficiently good approximations to achieve a high reconstruction fidelity with a simple quadratic metric amenable to a broad class of minimization methods. These preliminary simulation-based results are very promising and suggest that such methods may be used to produce high-fidelity reconstructions with less computation than many other statistical methods. By design, these quadratic methods are also explicit and quantitative in their description, allowing fine-tuning according to the specific uncertainties and noise model of the experiment. Further research is required to ascertain the full practical potential of these methods.

Proceedings Article | 9 September 2021 Presentation + Paper

Density estimation in x-ray computed tomography using the Alvarez-Macovski model

Qiheng Yang, Nirjhor Chakraborty, Dmitry Lakshtanov, Adrian Sheppard, Andrew Kingston

Proceedings Volume 11840, 118400E (2021) https://doi.org/10.1117/12.2595473

KEYWORDS: Calibration, Signal attenuation, X-rays, Quartz, Dual energy imaging, Minerals, Photon counting, Image filtering, Aluminum, Chalcopyrites

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Proceedings Article | 9 September 2021 Presentation + Paper

Least-squares and maximum-likelihood in computed tomography

Murdock Grewar, Glenn Myers, Andrew Kingston

Proceedings Volume 11840, 1184014 (2021) https://doi.org/10.1117/12.2595559

KEYWORDS: Data modeling, Reconstruction algorithms, X-rays, Signal attenuation, Statistical modeling, X-ray detectors, Computed tomography, X-ray computed tomography, Statistical inference, Statistical methods, Convex optimization

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Proceedings Article | 4 October 2016 Paper

High cone-angle x-ray computed micro-tomography with 186 GigaVoxel datasets

Glenn Myers, Shane Latham, Andrew Kingston, Jan Kolomazník, Václav Krajíček, Tomáš Krupka, Trond Varslot, Adrian Sheppard

Proceedings Volume 9967, 99670U (2016) https://doi.org/10.1117/12.2238258

KEYWORDS: Tomography, CT reconstruction, X-ray computed tomography, Radiography, X-rays, Reconstruction algorithms, X-ray imaging, Image resolution, Sensors, 3D image processing

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Proceedings Article | 4 October 2016 Presentation + Paper

Optimized x-ray source scanning trajectories for iterative reconstruction in high cone-angle tomography

Andrew Kingston, Glenn Myers, Shane Latham, Heyang Li, Jan Veldkamp, Adrian Sheppard

Proceedings Volume 9967, 996712 (2016) https://doi.org/10.1117/12.2238297

KEYWORDS: Tomography, X-ray sources, Radiography, X-rays, Sensors, Point spread functions, Data acquisition, Signal to noise ratio, Positron emission tomography, Computed tomography

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Proceedings Article | 3 October 2016 Paper

Rapidly converging multigrid reconstruction of cone-beam tomographic data

Glenn Myers, Andrew Kingston, Shane Latham, Benoit Recur, Thomas Li, Michael Turner, Levi Beeching, Adrian Sheppard

Proceedings Volume 9967, 99671M (2016) https://doi.org/10.1117/12.2238267

KEYWORDS: Tomography, Reconstruction algorithms, 3D image reconstruction, Radiography, 3D modeling, CT reconstruction, 3D image processing, Sensors, Imaging systems, Image quality

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Proceedings Article | 3 October 2016 Presentation + Paper

Multi-resolution radiograph alignment for motion correction in x-ray micro-tomography

Shane Latham, Andrew Kingston, Benoit Recur, Glenn Myers, Adrian Sheppard

Proceedings Volume 9967, 996710 (2016) https://doi.org/10.1117/12.2238259

KEYWORDS: Radiography, X-rays, Distortion, Reconstruction algorithms, Image resolution, Motion estimation, Computer simulations, Motion measurement, Sensors, Image registration

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Proceedings Article | 17 March 2015 Paper

Building a bone µ-CT images atlas for micro-architecture recognition

E. Freuchet, B. Recur, Jp. Guédon, A. Kingston, F. Autrusseau, Y. Amouriq

Proceedings Volume 9417, 94172Q (2015) https://doi.org/10.1117/12.2082807

KEYWORDS: Bone, 3D image processing, Mathematical morphology, Radiography, 3D acquisition, Tomography, 3D modeling, Reconstruction algorithms, Molybdenum, Visualization

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