# 2D PET Image Reconstruction Using Robust L1 Estimation of the Gaussian Mixture Model

## TL;DR

This paper proposes a novel PET image reconstruction method using Gaussian mixture models and robust L1 estimation, improving resolution and reducing noise compared to traditional pixel-based approaches.

## Contribution

It introduces a robust segmentation algorithm based on Gaussian mixture models and an iterative EM-like algorithm for PET image reconstruction.

## Key findings

- Enhanced image resolution and noise robustness.
- Reduced computational complexity and convergence time.
- Demonstrated effectiveness on PET imaging data.

## Abstract

An image or volume of interest in positron emission tomography (PET) is reconstructed from pairs of gamma rays emitted from a radioactive substance. Many image reconstruction methods are based on estimation of pixels or voxels on some predefined grid. Such an approach is usually associated with limited resolution of the reconstruction, high computational complexity due to slow convergence and noisy results. This paper explores reconstruction of PET images using the underlying assumption that the originals of interest can be modeled using Gaussian mixture models. A robust segmentation method based on statistical properties of the model is presented, with an iterative algorithm resembling the expectation-maximization algorithm. Use of parametric models for image description instead of pixels circumvent some of the mentioned limitations.