elsa: an elegant framework for tomographic reconstruction

Abstract

elsa is a versatile framework in the landscape of X-ray tomography, offering a powerful toolkit for developing iterative reconstruction algorithms. It is primarily designed for challenging applications in classical attenuation X-ray computed tomography (CT) and advanced modalities like Phase-Contrast X-ray CT (PCCT) or Anisotropic Dark-field tomography (AXDT). elsa stands out with an extensive set of building blocks and a unified abstraction, providing the means for high-quality reconstructions across a spectrum of imaging modalities and applications.Designed as an operator-and optimisation-based framework, elsa takes a mathematical approach to model the reconstruction pipeline of imaging modalities. Through formulating optimization problems and a suite of iterative reconstruction algorithms, elsa addresses challenges in attenuation X-ray CT, PCCT, and AXDT.The core of elsa (developed in modern C++ with GPU acceleration) ensures efficiency, while its Python interface allows easy accessibility for students and researchers.Distinctively, elsa positions itself as a unique solution by supporting modern iterative reconstruction techniques for novel X-ray CT imaging modalities. The framework addresses challenges associated with X-ray CT, such as arbitrary trajectories, automatic differentiation, differential signals for PCCT, and spherical function valued reconstructions for AXDT, offering a simple abstraction for implementing tailored reconstruction methods.