Johannes B. Thalhammer, Tina Dorosti, Sebastian Peterhansl, Florian Schaff, Daniela Pfeiffer, Franz Pfeiffer, Martin Donnelley, Ronan Smith, Marcus Kitchen, Jannis Ahlers, Lucy Costello, Lorenzo D’Amico, Kaye Morgan
https://j-3to.github.io/Neighbor2Inverse/
Abstract— Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong clinical potential. However, safe translation requires substantial radiation dose reduction, which inevitably increases image noise. Supervised Convolutional Neural Network-based denoising can restore image quality but depends on paired low- and high-dose datasets, which are rarely available in practice. Self-supervised methods avoid this limitation, yet most are not well adapted to the inverse problem of PBI computed tomography (CT).