Improving throughput and image quality of high-resolution 3D X-ray microscopes using deep learning reconstruction techniques
Carl Zeiss Industrial Metrology, Herminso Villarraga-Gómez, Carl Zeiss X-ray Microscopy, Mansoureh Norouzi Rad, Carl Zeiss X-ray Microscopy, Matthew Andrew, Carl Zeiss X-ray Microscopy, Andriy Andreyev, Carl Zeiss X-ray Microscopy, Ravikumar Sanapala, Carl Zeiss, Lars Omlor, Carl Zeiss X-ray Microscopy, Christoph Graf vom Hagen
Abstract
In high-resolution X-ray computed tomography (CT), also known as 3D X-ray microscopy (XRM), low photon counts can lead to extremely long data acquisition times (in the order of hours). Reducing the number of radiographic projections (Np) acquired for CT reconstruction can be a cost-efficient solution in some cases. But the risk associated with reducing Np, if analytical filteredbackprojection algorithms are used for CT reconstruction, e.g., Feldkamp-Davis-Kress (FDK), is that it may produce a significant loss of image quality. Typical Np thresholds for a faithful 3D image reconstruction, required by the Nyquist-Shannon sampling theorem, are in the order of thousand projection views with modern XRM instruments. It is now well known, however, that deep learning (DL) based algorithms for CT reconstruction can improve the scan time (throughput) and image quality capabilities of XRM. This paper proposes the use of DL-based algorithms as an option for reducing Np, even down to a few hundred projections, without a significant loss of image quality. The integration of DL-based reconstruction techniques into 3D XRM workflows is presented throughout this article. It is shown that 3D XRM data reconstructions produced by DL-based workflows can provide up to 8X and 10X throughput improvement at similar or better image quality compared to standard FDK reconstruction.