Kuang Gong
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View article: First evaluation in multiple sclerosis using PET tracer [18F]3F4AP demonstrates heterogeneous binding across lesions
First evaluation in multiple sclerosis using PET tracer [18F]3F4AP demonstrates heterogeneous binding across lesions Open
NCT04699747, Start date March 25th, 2021.
View article: SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Open
Although new vision foundation models such as Segment Anything Model 2 (SAM2) have significantly enhanced zero-shot image segmentation capabilities, reliance on human-provided prompts poses significant challenges in adapting SAM2 to medica…
View article: CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation Open
Most publicly available medical segmentation datasets are only partially labeled, with annotations provided for a subset of anatomical structures. When multiple datasets are combined for training, this incomplete annotation poses challenge…
View article: An efficient parallel DCNN algorithm in big data environment
An efficient parallel DCNN algorithm in big data environment Open
Big data plays a vital role in developing remote sensing, landslide prediction, and enabling applications, the integration of deep convolutional neural networks (DCNN) has significantly improved its prediction accuracy. However, several ch…
View article: Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis Open
Vision-language models (VLMs) have shown promise in 2D medical image analysis, but extending them to 3D remains challenging due to the high computational demands of volumetric data and the difficulty of aligning 3D spatial features with cl…
View article: Geodesic Diffusion Models for Efficient Medical Image Enhancement
Geodesic Diffusion Models for Efficient Medical Image Enhancement Open
Diffusion models generate data by learning to reverse a forward process, where samples are progressively perturbed with Gaussian noise according to a predefined noise schedule. From a geometric perspective, each noise schedule corresponds …
View article: DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Open
Vision-language models (VLMs) have been widely applied to 2D medical image analysis due to their ability to align visual and textual representations. However, extending VLMs to 3D imaging remains computationally challenging. Existing 3D VL…
View article: Imaging Demyelinated Axons After Spinal Cord Injuries with PET Tracer [ <sup>18</sup> F]3F4AP
Imaging Demyelinated Axons After Spinal Cord Injuries with PET Tracer [ <sup>18</sup> F]3F4AP Open
Spinal cord injuries (SCIs) often lead to lifelong disability. Among the various types of injuries, incomplete and discomplete injuries, where some axons remain intact, offer potential for recovery. However, demyelination of these spared a…
View article: LDM-Morph: Latent diffusion model guided deformable image registration
LDM-Morph: Latent diffusion model guided deformable image registration Open
Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutional neural networks (CNNs) or Transformers to learn features …
View article: Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model Open
Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-gene…
View article: Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet Open
Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Cur…
View article: Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation Open
Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for disease diagnosis and treatment planning. This is primarily…
View article: Imaging demyelinated axons after spinal cord injuries with PET tracer [<sup>18</sup>F]3F4AP
Imaging demyelinated axons after spinal cord injuries with PET tracer [<sup>18</sup>F]3F4AP Open
Spinal cord injuries (SCI) often lead to lifelong disability. Among the various types of injuries, incomplete and discomplete injuries, where some axons remain intact, offer potential for recovery. However, demyelination of these spared ax…
View article: A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches Open
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photo…
View article: Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising
Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising Open
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). Recently convolutional neural networks (CNNs) have been widely…
View article: PET image denoising based on denoising diffusion probabilistic model
PET image denoising based on denoising diffusion probabilistic model Open
View article: SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images
SwinCross: Cross‐modal Swin transformer for head‐and‐neck tumor segmentation in PET/CT images Open
Background 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 for radiotherapy planning but a time‐consuming …
View article: TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models Open
In this work, we developed a novel text-guided image synthesis technique which could generate realistic tau PET images from textual descriptions and the subject's MR image. The generated tau PET images have the potential to be used in exam…
View article: Impact of motion correction on [<sup>18</sup>F]-MK6240 tau PET imaging
Impact of motion correction on [<sup>18</sup>F]-MK6240 tau PET imaging Open
Objective . Positron emission tomography (PET) imaging of tau deposition using [ 18 F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly deg…
View article: SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images Open
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 for radiotherapy planning but a time-consuming process. In…
View article: Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation Open
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 flocked to Transformer-based networks for various main stream…
View article: Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction
Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction Open
Image reconstruction of low-count positron emission tomography (PET) data is challenging. Kernel methods address the challenge by incorporating image prior information in the forward model of iterative PET image reconstruction. The kerneli…
View article: PET image denoising based on denoising diffusion probabilistic models
PET image denoising based on denoising diffusion probabilistic models Open
Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic models (DDPM) are distribution learning-based models, which try to transform a nor…
View article: Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising Open
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). Recently convolutional neural networks (CNNs) have been widely…
View article: A Noise-level-aware Framework for PET Image Denoising
A Noise-level-aware Framework for PET Image Denoising Open
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 in that region. The number of counts in a region depends, in…
View article: Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction
Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction Open
Image reconstruction of low-count positron emission tomography (PET) data is challenging. Kernel methods address the challenge by incorporating image prior information in the forward model of iterative PET image reconstruction. The kerneli…
View article: A Noise-Level-Aware Framework for PET Image Denoising
A Noise-Level-Aware Framework for PET Image Denoising Open
View article: Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network
Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network Open
View article: Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior Open
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 an integrated framework. Due to limited counts received, sig…
View article: A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records
A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records Open