113 citations · 237 across the 15 of their papers we have counts for
15 papers
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Gary Y. Li, Junyu Chen, Se-In Jang +2
Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite…
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Ye Li, Junyu Chen, Se-in Jang +2
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have fl…
PET image denoising based on denoising diffusion probabilistic models
Kuang Gong, Keith A. Johnson, Georges El Fakhri +2
Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic models (DDPM) are distr…
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
Se-In Jang, Tinsu Pan, Ye Li +4
Position emission tomography (PET) is widely used in clinics and research due to its quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR). Rec…
A Noise-level-aware Framework for PET Image Denoising
Ye Li, Jianan Cui, Junyu Chen +7
In PET, the amount of relative (signal-dependent) noise present in different body regions can be significantly different and is inherently related to the number of counts present i…
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…
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior
Kuang Gong, Ciprian Catana, Jinyi Qi +1
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in a…
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…
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Nuobei Xie, Kuang Gong, Ning Guo +5
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology informa…
MR-Based PET Attenuation Correction using a Combined Ultrashort Echo Time/Multi-Echo Dixon Acquisition
Paul Kyu Han, Debra E. Horng, Kuang Gong +7
We propose a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LAC) in positron emission tomography (PET) using a physical compartm…
Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Nuobei Xie, Kuang Gong, Ning Guo +4
Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors i…
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
Dufan Wu, Kuang Gong, Kyungsang Kim +1
Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired…
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
Kuang Gong, Kyungsang Kim, Jianan Cui +4
Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application…
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Kuang Gong, Jaewon Yang, Kyungsang Kim +3
Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation corr…
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Kuang Gong, Jiahui Guan, Kyungsang Kim +4
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and su…