Tomo-center: an AI-based rotation-axis center finder for synchrotron micro- and nano-tomography

Abstract

Accurate determination of the rotation-axis position is a prerequisite for artifact-free reconstruction in parallel-beam synchrotron micro-tomography. Traditional approaches such as Vo's method rely on sinogram features that can fail for low-contrast or weakly absorbing specimens. We present a learning-based method that treats center selection as a binary classification problem, using a DINOv2-pretrained vision transformer aggregated with attention-based multiple-instance learning, fine-tuned end-to-end on tomographic images. At inference time, the proposed algorithm was applied to a stack of tomograms reconstructed at a sweep of candidate centers to select the optimal center for reconstruction. We tested the estimation accuracy of the proposed method on two independent data sources and consistently achieved a mean absolute error of below 1 pixel. We also tested the method robustness to sparse or noisy acquisitions with the same datasets and demonstrated consistent performance when the number of projections was reduced by a factor of up to 10 or the blank scan factor of the underlying Poisson's noise was increased to 10. We also illustrated the interpretability of the proposed method by mapping out the relative contributions of continuous spatial features to the overall classification task. This method, delivered as tomo-center, an open-source command-line tool, has been integrated into several tomography software packages to assist experiments during the routine beamline operations.