Iterative X-ray spectroscopic ptychography.
Huibin ChangZiqin RongPablo EnfedaqueStefano MarchesiniPublished in: Journal of applied crystallography (2020)
Spectroscopic ptychography is a powerful technique to determine the chemical composition of a sample with high spatial resolution. In spectro-ptychography, a sample is rastered through a focused X-ray beam with varying photon energy so that a series of phaseless diffraction data are recorded. Each chemical component in the material under investigation has a characteristic absorption and phase contrast as a function of photon energy. Using a dictionary formed by the set of contrast functions of each energy for each chemical component, it is possible to obtain the chemical composition of the material from high-resolution multi-spectral images. This paper presents SPA (spectroscopic ptychography with alternating direction method of multipliers), a novel algorithm to iteratively solve the spectroscopic blind ptychography problem. First, a nonlinear spectro-ptychography model based on Poisson maximum likelihood is designed, and then the proposed method is constructed on the basis of fast iterative splitting operators. SPA can be used to retrieve spectral contrast when considering either a known or an incomplete (partially known) dictionary of reference spectra. By coupling the redundancy across different spectral measurements, the proposed algorithm can achieve higher reconstruction quality when compared with standard state-of-the-art two-step methods. It is demonstrated how SPA can recover accurate chemical maps from Poisson-noised measurements, and its enhanced robustness when reconstructing reduced-redundancy ptychography data using large scanning step sizes is shown.
Keyphrases
- high resolution
- dual energy
- molecular docking
- optical coherence tomography
- magnetic resonance
- electron microscopy
- deep learning
- machine learning
- computed tomography
- contrast enhanced
- image quality
- electronic health record
- mass spectrometry
- big data
- monte carlo
- convolutional neural network
- molecular dynamics simulations
- data analysis