TomoSLAM: factor graph optimization for rotation angle refinement in microtomography

TL;DR

This paper introduces TomoSLAM, a novel approach that applies SLAM techniques to refine rotation angles in microtomography, improving image reconstruction accuracy by leveraging feature matching and factor graph optimization.

Contribution

It presents a new method that models trajectory refinement in microtomography as a SLAM problem, integrating feature-based matching with factor graph optimization.

Findings

Improved rotation angle accuracy in microtomography reconstructions.

Effective use of SURF features and RANSAC for feature matching.

Enhanced reconstruction quality through trajectory refinement.

Abstract

In computed tomography (CT), the relative trajectories of a sample, a detector, and a signal source are traditionally considered to be known, since they are caused by the intentional preprogrammed movement of the instrument parts. However, due to the mechanical backlashes, rotation sensor measurement errors, thermal deformations real trajectory differs from desired ones. This negatively affects the resulting quality of tomographic reconstruction. Neither the calibration nor preliminary adjustments of the device completely eliminates the inaccuracy of the trajectory but significantly increase the cost of instrument maintenance. A number of approaches to this problem are based on an automatic refinement of the source and sensor position estimate relative to the sample for each projection (at each time step) during the reconstruction process. A similar problem of position refinement while observing different images of an object from different angles is well known in robotics (particularly, in mobile robots and self-driving vehicles) and is called Simultaneous Localization And Mapping (SLAM). The scientific novelty of this work is to consider the problem of trajectory refinement in microtomography as a SLAM problem. This is achieved by extracting Speeded Up Robust Features (SURF) features from X-ray projections, filtering matches with Random Sample Consensus (RANSAC), calculating angles between projections, and using them in factor graph in combination with stepper motor control signals in order to refine rotation angles.