A Neural Network for Denoising Multispectral Tomography Data at BM18 of the European Synchrotron Radiation Facility

Multispectral imaging is susceptible to high levels of noise due to the low photon flux observed, particularly in narrow energy bins. To address this issue, multispectral data was acquired at the BM18 beamline of the ESRF using a silicon prism array to refract a polychromatic beam into energy bins, which were then captured by an SCMOS detector. Unlike photon counting detectors, which suffer from pile-up and low energy resolution, the proposed method can handle the high flux of the unfiltered BM18 beam and split the beam into 100 energy bins. The resulting data, which was significantly affected by noise, was denoised using a neural network that had been trained on simulated data. The efficacy of the denoising method was evaluated on computed tomography scans of two phantoms containing high and low atomic number (Z) materials, as well as other samples. Despite differences between the simulation and experimental setups, the network demonstrated a significant reduction in noise, as illustrated in the projections and reconstructed slices. The denoised reconstructions exhibited a notable suppression of noise without compromising image detail or introducing additional artifacts, emphasizing the model's potential for enhancing multispectral imaging in material analysis.