The reconstruction of SAXS tomography data is equally challenging due to their high dimensionality. A possible traditional approach consists in calculating invariants of the SAXS data before reconstruction, which replaces the three-dimensional SAXS data by scalars that can be reconstructed much more efficiently . SAXS invariants are useful, since they contain information about volume and surface of nano-size objects in the specimen and allow, for example, the calculation of particle sizes in bone or dentin . In the last few years, ML approaches are being developed for tomographic data reconstruction. Omori and coworkers review these developments for tomography using SAXS but also xray diffraction and other modalities . While these advances relate to step (D) in Fig. , the review also addresses ML approaches for segmentation and analysis of the reconstructed data (step (E) in Fig. ).