Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to $^{90}$Y-PET

TL;DR

This paper introduces a post-processing method for PET imaging that removes negative voxel intensities while maintaining local mean activity, improving image interpretability and reducing variance in low-activity regions.

Contribution

A novel post-processing algorithm based on linear programming that preserves local mean activity and effectively removes negative intensities in PET images.

Findings

Lower variance in cold areas after post-processing

Efficient linear programming solution for negative intensity correction

Improved interpretability of PET images with low activity

Abstract

In a low-statistics PET imaging context, the positive bias in regions of low activity is a burning issue. To overcome this problem, algorithms without the built-in non-negativity constraint may be used. They allow negative voxels in the image to reduce, or even to cancel the bias. However, such algorithms increase the variance and are difficult to interpret, since negative radioactive concentrations have no physical meaning. Here, we propose a post-processing strategy to remove negative intensities while preserving the local mean activities. Our idea is to transfer the negative intensities to neighboring voxels, so that the mean of the image is preserved. The proposed post-processing algorithm solves a linear programming problem with a specific symmetric structure, and the solution can be computed in a very efficient way. Acquired data from an yttrium-90 phantom show that on images produced by a non-constrained algorithm, a much lower variance in the cold area is obtained after the post-processing step.