LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method

TL;DR

LMPDNet introduces a novel deep learning method for real-time TOF-PET list-mode image reconstruction, significantly outperforming traditional algorithms and demonstrating the advantages of list-mode data over sinogram data.

Contribution

The paper presents LMPDNet, a new model-based deep learning approach that efficiently reconstructs TOF-PET images from list-mode data, addressing memory and computation challenges.

Findings

LMPDNet outperforms traditional reconstruction algorithms.

List-mode data shows superior spatial and temporal efficiency.

Real-time reconstruction is feasible with the proposed method.

Abstract

The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) yields improved image properties. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we present a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We address the issue of real-time parallel computation of the projection matrix for list-mode data, and propose an iterative model-based module that utilizes a dedicated network model for list-mode data. Our experimental results indicate that the proposed LMPDNet outperforms traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compare the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.