19 citations · 19 across the 6 of their papers we have counts for
6 papers
Manifold-Constrained PET Reconstruction with Learned Flow-Matching Priors
Hengjia Ran, Jie Luo, Yutao Zhu +3
Image reconstruction for positron emission tomography (PET) is an ill-posed Poisson inverse problem that often suffers from severe noise amplification and artifacts. In this work,…
Deep unrolled primal dual network for TOF-PET list-mode image reconstruction
Rui Hu, Chenxu Li, Kun Tian +3
Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of PET reconstruction images and reducing noise. List-…
DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction
Rui Hu, Yunmei Chen, Kyungsang Kim +3
Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heav…
STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction
Rui Hu, Jianan Cui, Chengjin Yu +2
Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatia…
LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method
Chenxu Li, Rui Hu, Jianan Cui +1
The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) yields improved image properties. However, implementing the…
TransEM:Residual Swin-Transformer based regularized PET image reconstruction
Rui Hu, Huafeng Liu
Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural net…