Multi-Modal Ptychography: Recent Developments and Applications

Abstract

Utilisation of partially coherent X-rays in Coherent X-ray Diffraction Imaging (CDI) and X-ray ptychography have brought intense research and developments for the past few years. Generally, data reconstructions convergence can be achieved by considering to incorporate partially coherent X-rays in ptychographic iterative reconstructions algorithms. Recently, exploration of both X-ray probe and sample modes have been studied and it was suggested that sufficient constraints are required to achieve accurate data reconstructions due to the mathematical inseparability of multiple sample modes that are superimposed into a single set of ptychographic intensity patterns. Here we review the multi-modal ptychography that can be used as a new emerging technique to investigate dynamical sample systems with partially coherent X-rays by recent experimental and simulated demonstrations.

A Parameter Refinement Method for Ptychography Based on Deep Learning Concepts

“…Even if in the literature, many solutions have been proposed, in most cases, the problems are typically tackled independently. Partial coherence and mixed-state ptychography have been extensively reviewed (e.g., in [3,27,28]); the same is valid for the position refinement problem [29][30][31][32][33][34][35], with methods that are quite similar to what is performed in, e.g., super-resolution imaging [36] or CT [37]. To understand the importance of positions for ptychography, Figure 4 illustrates a slightly exaggerated condition: note that the entire object computational box changes format, and this is detrimental, especially from the implementation point of view.…”

“…Browsing through the ptychography corpus, it appears that the majority of the attention in automatic correction is devoted mainly to the first three flaws; partial coherence and mixed state ptychography has been extensively reviewed in the literature (e.g. in 6,27,28 ), while only in 8 an evolutionary algorithm is used to cope with the uncertainty on the source-tosample distance in electron ptychography, that translates in a spherical aberration of the probe; in the context of 8 (electron ptychography), indeed, conventional probe-solving algorithms like EPIE 29 and DM 22 would fail 8 then, the reconstruction is carried out just using the conventional PIE algorithm 29 which requires a well-defined a-priori probe function. In 30 the authors propose to use a position refinement scheme also to correct for the axial parameters, as the latter is responsible to modifying the distances between adjacent probes, while in the very recent (January 2021) work 31 , the authors use an autograd environment to infer directly the propagation distance, but the resulting method is tested on visible light only.…”

Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy

“…When multiple propagating modes interfere, the resulting diffraction pattern appears blurred [ 107 ]. Based on the decomposition of the mutual intensity function [ 108 ], probe decomposition techniques such as [ 109 , 110 , 111 , 112 ] are often employed to computationally disentangle multiple modes.…”

Resolution Enhancement in Coherent Diffraction Imaging Using High Dynamic Range Image

“…Due to its advantages such as no reference light wave being required and a low requirement on optical device processing accuracy, it has been actively applied in many fields, such as nanomaterials science [7], industrial measurement [8], and biomedical science [9]. In recent years, many researchers have proposed various modifications to improve the performance of CDI to meet requirements in different applications, including ptychographic iterative engine (PIE) [10][11][12][13], coherent modulation imaging [14][15][16], Fourier ptychographic microscopy [17,18], etc. [19][20][21].…”