LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method
This work addresses a specific bottleneck in medical imaging for PET reconstruction, offering an incremental improvement in efficiency and performance.
The study tackled the challenge of high memory requirements in model-based deep learning for TOF-PET reconstruction by proposing LMPDNet, a novel method that outperformed traditional iterative algorithms in image reconstruction from list-mode data.
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.