DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data

TL;DR

This paper introduces DirectPET, a neural network capable of reconstructing full-size multi-slice PET images directly from sinogram data, offering faster processing with comparable quality to traditional methods.

Contribution

The paper presents a novel Radon inversion layer enabling large-scale neural PET reconstruction of multi-slice volumes from sinograms, improving efficiency and potential clinical applicability.

Findings

DirectPET produces images similar to OSEM in quality

Reconstructs multi-slice volumes faster than traditional methods

Maintains image quality in low-dose scenarios

Abstract

Purpose: Neural network image reconstruction directly from measurement data is a relatively new field of research, that until now has been limited to producing small single-slice images (e.g., 1x128x128). This paper proposes a novel and more efficient network design for Positron Emission Tomography called DirectPET which is capable of reconstructing multi-slice image volumes (i.e., 16x400x400) from sinograms. Approach: Large-scale direct neural network reconstruction is accomplished by addressing the associated memory space challenge through the introduction of a specially designed Radon inversion layer. Using patient data, we compare the proposed method to the benchmark Ordered Subsets Expectation Maximization (OSEM) algorithm using signal-to-noise ratio, bias, mean absolute error and structural similarity measures. In addition, line profiles and full-width half-maximum measurements are provided for a sample of lesions. Results: DirectPET is shown capable of producing images that are quantitatively and qualitatively similar to the OSEM target images in a fraction of the time. We also report on an experiment where DirectPET is trained to map low count raw data to normal count target images demonstrating the method's ability to maintain image quality under a low dose scenario. Conclusion: The ability of DirectPET to quickly reconstruct high-quality, multi-slice image volumes suggests potential clinical viability of the method. However, design parameters and performance boundaries need to be fully established before adoption can be considered.