Fast conjugate gradient algorithm extension for analyzer-based imaging reconstruction

Oriol Caudevilla; Jovan G. Brankov

doi:10.1117/12.2217164

1 April 2016 Fast conjugate gradient algorithm extension for analyzer-based imaging reconstruction

Oriol Caudevilla, Jovan G. Brankov

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97834J (2016) https://doi.org/10.1117/12.2217164
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

This paper presents an extension of the classic Conjugate Gradient Algorithm. Motivated by the Analyzer-Based Imaging inverse problem, the novel method maximizes the Poisson regularized log-likelihood with a non-linear transformation of parameter faster than other solutions. The new approach takes advantage of the special properties of the Poisson log-likelihood to conjugate each ascend direction with respect all the previous directions taken by the algorithm. Our solution is compared with the general solution for non-quadratic unconstrained problems: the Polak- Ribiere formula. Both methods are applied to the ABI reconstruction problem.

Citation Download Citation

Oriol Caudevilla and Jovan G. Brankov "Fast conjugate gradient algorithm extension for analyzer-based imaging reconstruction", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834J (1 April 2016); https://doi.org/10.1117/12.2217164

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