# Diffusion-based Sinogram Interpolation for Limited Angle PET

arXiv: https://arxiv.org/abs/2511.09383 · HTML: https://mlsanity.com/paper/2511.09383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction](https://mlsanity.com/paper/2605.08030/) (2026)

## Abstract (arXiv)

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.