An Edge Alignment-based Orientation Selection Method for Neutron Tomography

TL;DR

This paper introduces an adaptive orientation selection method for neutron tomography that leverages model-based iterative reconstruction to reduce measurement requirements while maintaining high image quality.

Contribution

The proposed method adaptively selects orientations based on edge alignment and diversity, improving efficiency over traditional fixed-angle sampling in neutron tomography.

Findings

Achieves high-quality reconstructions with fewer measurements.

Demonstrates effectiveness on simulated and experimental data.

Reduces acquisition time significantly.

Abstract

Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.