Data-driven deformation correction in X-ray spectro-tomography with implicit neural networks.

Abstract

Full-field transmission X-ray microscopy with X-ray absorption near-edge structure spectroscopy enables non-destructive, high-resolution, chemically specific three-dimensional morphological and compositional analyses. However, spectro-tomographic acquisitions often suffer from image deformations and misalignments caused by mechanical instabilities and hardware limitations, which can substantially degrade the quality of tomographic reconstruction and downstream analyses. This critical bottleneck hinders the broader application of X-ray spectro-tomography in addressing complex scientific problems across various disciplines. To address this, we introduce CANet, a self-supervised coordinate-based neural network that implicitly models deformation fields to efficiently and accurately correct misalignment. Unlike traditional methods, CANet requires no external training data and learns a continuous mapping from projection spectral or angular coordinates to affine transformations, enabling unified registration across both tomographic and spectral dimensions. Demonstrated on X-ray spectro-tomographic datasets of battery cathode particles, CANet achieves robust alignment and restores high-fidelity structural and chemical contrast, thereby facilitating the resolution of nanoscale degradation mechanisms.