Three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit

Abstract

Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading towards the violation of these assumptions, by combining the high penetration power of x-rays which enables thick specimens to be imaged, with improved spatial resolution which decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation, and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy, and for coherent scanning techniques like ptychography. Our implementation utilizes the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, which demonstrates a much straightforward way to solve optimization problems in computational imaging, and endows our program great flexibility and portability.

References in corpus (4)

• X-ray image reconstruction from a diffraction pattern alone
• High-resolution ab initio three-dimensional X-ray diffraction microscopy
• Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction
• Relative merits and limiting factors for x-ray and electron microscopy of thick, hydrated organic materials (revised)

Cited by in corpus (13)

• Machine Learning on Neutron and X-Ray Scattering
• Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation
• Efficient and flexible approach to ptychography using an optimization framework based on automatic differentiation
• X-ray phase-contrast imaging: a broad overview of some fundamentals
• Artifact Identification in X-ray Diffraction Data using Machine Learning Methods
• Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics
• Reconstructing the Scattering Matrix from Scanning Electron Diffraction Measurements Alone
• Kernel Phase and Coronagraphy with Automatic Differentiation
• Near, far, wherever you are: simulations on the dose efficiency of holographic and ptychographic coherent imaging
• PtyRAD: A High-performance and Flexible Ptychographic Reconstruction Framework with Automatic Differentiation
• Using a modified double deep image prior for crosstalk mitigation in multislice ptychography
• Noise-robust latent vector reconstruction in ptychography using deep generative models
• Fidelity-preserving enhancement of ptychography with foundational text-to-image models