Diffusion-based Sinogram Interpolation for Limited Angle PET

Abstract--Accurate PET imaging increasingly requires methods that support unconstrained detector layouts--from walk-through designs to long-axial rings--where gaps and open sides lead to severely undersampled sinograms. Instead of constraining the hardware to form complete cylinders, we propose treating the missing lines-of-responses as a learnable prior . Data-driven approaches, particularly generative models, offer a promising pathway to recover this missing information. In this work, we explore the use of conditional diffusion models to interpolate sparsely sampled sinograms, paving the way for novel, cost-efficient, and patient-friendly PET geometries in real clinical settings. Positron Emission Tomography (PET) relies on the coincidence detection of gamma photon pairs using scintillation crystals.