Diffusion-based Sinogram Interpolation for Limited Angle PET

TL;DR

This paper introduces a diffusion-based method for interpolating missing sinogram data in limited-angle PET scans, enabling flexible detector configurations and potentially reducing costs and complexity.

Contribution

It proposes a novel use of conditional diffusion models to accurately interpolate undersampled sinograms in PET imaging, improving flexibility over traditional hardware constraints.

Findings

Diffusion models effectively interpolate missing sinogram data.

The method supports flexible PET detector geometries.

Potential for cost reduction and improved patient comfort.

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.