Effects of fast x-ray cone-beam tomographic measurement on dimensional metrology

Abstract

X-ray computed tomography (XCT) is increasingly used for dimensional metrology, where it can offer accurate measurements of internal features that are not accessible with other techniques. However, XCT scanning can be relatively slow, which often prevents routine uptake for many applications. This paper explores the feasibility of improving the speed of XCT measurements whilst maintaining the quality of the dimensional measurements derived from reconstructed volumes. In particular, we compare two approaches to fast XCT acquisition, the use of fewer XCT projections as well as the use of shortened x-ray exposure times for each projection. The study shows that the additional Poisson noise produced by reducing the exposure for each projection has significantly less impact on dimensional measurements compared to the artefacts associated with strategies that take fewer projection images, leading to about half the measurement error variability. Advanced reconstruction algorithms such as the conjugate gradient least squares method or total variation constrained approaches, are shown to allow further improvements in measurement speed, though this can come at the cost of increased measurement bias (e.g. 2.8 % increase in relative error in one example) and variance (e.g. 25 % in the same example).

Editorial for the Metrologia Focus Issue on Length Metrology

“…However the transition from using this process to produce 'images' to performing traceable metrology requires careful consideration of the sources of error. One such aspect that is being addressed is that of accuracy degradation when performing fast measurements [15].…”

On Krylov methods for large-scale CBCT reconstruction

“…Moreover, while in μ-CT the radiation dose is not harmful for the imaged object, there are cases where a sparse or low dose sampling is still required. For example, for non destructive testing of manufacturing processes, the throughput of the scanning should align with the throughput of the production, so the measuring speed limits the amount of data that can be acquired (Rossides et al 2022). However, one needs to mention that iterative methods are slow compared to FDK: this is because FDK is computed by a ramp filter and a back-projection; whereas all existing iterative method compute, at least, one forward projection and one back-projection per iteration, and therefore each iteration requires almost the same computation time than FDK.…”