20 citations · 56 across the 21 of their papers we have counts for
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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-…
Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction
Yubo Ye, Huafeng Liu, Linwei Wang
In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solu…
Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers
Huafeng Liu, Benjamin Dowdell, Todd Engelder +3
Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (M…
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…
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network
Jianan Cui, Kuang Gong, Paul Han +2
Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a smal…
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Ming Li, Weiwei Zhang, Guang Yang +5
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps acr…