New Journal of Physics· 2012Q1
Maximum-likelihood refinement for coherent diffractive imaging
- 411citations
- Q1SCImago
- 2012year
Short summary
Maximum-likelihood (ML) refinement significantly improves coherent diffractive imaging reconstructions, enabling up to a tenfold reduction in X-ray dose while maintaining reconstruction quality.
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Key points
- Maximum-likelihood (ML) principles are applied to coherent diffractive imaging reconstruction.
- ML refinement shows significant improvements in signal-to-noise ratio, resolution, and sensitivity.
- Experimental X-ray data demonstrated gains in ML refinement.
- ML refinement allows for up to a tenfold reduction in X-ray dose for similar reconstruction quality.
AI-generated from the title and abstract; the full text is not read.
Abstract
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. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.
The authors' abstract, as published at the source. New Journal of Physics, 2012 · DOI ↗
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Field: Radiation
RadiationPhysics and Astronomy