Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation

TL;DR

Adorym is a versatile, optimization-based x-ray image reconstruction framework that uses automatic differentiation to refine parameters and improve image quality across various imaging methods and datasets.

Contribution

It introduces a generic framework capable of handling multiple x-ray imaging techniques with automatic differentiation for parameter refinement.

Findings

Enhanced image quality through parameter optimization

Supports large datasets with parallel processing

Applicable to diverse x-ray imaging methods

Abstract

We describe and demonstrate an optimization-based x-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to and fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement.