13 citations · 31 across the 8 of their papers we have counts for
8 papers
Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient results
Yansong Zhu, Siqi Li, Zhaoheng Xie +9
X-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral C…
Single-Subject Deep-Learning Image Reconstruction with a Neural Optimization Transfer Algorithm for PET-enabled Dual-Energy CT Imaging
Siqi Li, Yansong Zhu, Benjamin A. Spencer +1
Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgra…
Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction
Siqi Li, Kuang Gong, Ramsey D. Badawi +3
Image reconstruction of low-count positron emission tomography (PET) data is challenging. Kernel methods address the challenge by incorporating image prior information in the forwa…
Deep Kernel Representation for Image Reconstruction in PET
Siqi Li, Guobao Wang
Image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address th…
View-Guided Point Cloud Completion
Xuancheng Zhang, Yutong Feng, Siqi Li +5
This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address…
Modified Kernel MLAA Using Autoencoder for PET-enabled Dual-Energy CT
Siqi Li, Guobao Wang
Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PETenabled dual-energy CT combines a low-energy x-ray CT image with a high-e…
Low-Dose CT Image Denoising Using Parallel-Clone Networks
Siqi Li, Guobao Wang
Deep neural networks have a great potential to improve image denoising in low-dose computed tomography (LDCT). Popular ways to increase the network capacity include adding more lay…
Attention-based Multi-modal Fusion Network for Semantic Scene Completion
Siqi Li, Changqing Zou, Yipeng Li +2
This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the o…