Maximum-likelihood refinement for coherent diffractive imaging

By Pierre Thibault, M. Guizar‐Sicairos. 2012

403 citations recorded by the source corpus.

We introduce the application of maximum-likelihood (ML) principles to the image reconstruction problem in coherent diffractive imaging. We describe an implementation of the optimization procedure for ptychography, using conjugate gradients and including preconditioning strategies, regularization and typical modifications of the statistical noise model. The optimization principle is compared to a difference map reconstruction algorithm. With simulated data important improvements are observed, as measured by a strong increase in the signal-to-noise ratio. Significant gains in resolution and sensitivity are also demonstrated in the ML refinement of a reconstruction from experimental x-ray data. The immediate consequence of our results is the possible reduction of exposure, or dose, by up to an order of magnitude for a reconstruction quality similar to iterative algorithms currently in use.

Subfields: Geophysics, Radiation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/36771bd48c4b9ae09627d2ee0f1b0f862ad811d7

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In Radiation

In Radiology, Nuclear Medicine and Imaging

M. Soltanolkotabi

Citations in the corpus

Subwavelength coherent imaging of periodic samples using a 13.5 nm tabletop high-harmonic light source (2017, methodology): “The implementation of MEP consists of taking the current probe guess, propagating it to the detector where the measured intensity is enforced (following equation (3) in the main text), then back propagating the new guess to the sample plane (equation (4) in the main text), where it re-enters the ePI”
Experimental robustness of Fourier Ptychography phase retrieval algorithms (2015, methodology): “If we have information about the statistics of the noise, we can use it in our optimization formulation via the maximum likelihood estimation framework [29].”