Especially in SAXS tomography experiments, radiation damage should not be underestimated., since every specimen position will be hit several times by an intensive x-ray beam due to the required rotation of the specimen around multiple axes . A typical strategy is then to reduce the irradiation time, which inevitably increases the noise in the data. To avoid problems with this noise in the 6D data reconstruction after the measurements, Zhou and coworkers propose a machine learning (ML) for the denoising of scattering data . This approach facilitates step (D) in the diagram of Fig. .