Diffusion-based Sinogram Interpolation for Limited Angle PET
This addresses the challenge of accurate PET imaging with unconstrained hardware designs for clinical applications, though it appears incremental as it applies existing diffusion models to a specific domain problem.
The paper tackles the problem of incomplete PET sinograms from unconventional detector layouts by proposing a conditional diffusion model to interpolate missing data, enabling cost-efficient and patient-friendly PET geometries.
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.