Search Results for author: Kuang Gong

Found 14 papers, 1 papers with code

SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images

no code implementations8 Feb 2023 Gary Y. Li, Junyu Chen, Se-In Jang, Kuang Gong, Quanzheng Li

Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions. To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR.

Image Segmentation Tumor Segmentation

Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation

no code implementations21 Dec 2022 Ye Li, Junyu Chen, Se-In Jang, Kuang Gong, Quanzheng Li

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 medical tasks such as classification, segmentation, and estimation.

Tumor Segmentation

PET image denoising based on denoising diffusion probabilistic models

no code implementations13 Sep 2022 Kuang Gong, Keith A. Johnson, Georges El Fakhri, Quanzheng Li, Tinsu Pan

Regional and surface quantification shows that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference can achieve the best performance.

Image Denoising

Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising

no code implementations7 Sep 2022 Se-In Jang, Tinsu Pan, Ye Li, Pedram Heidari, Junyu Chen, Quanzheng Li, Kuang Gong

In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs.

Image Denoising

A Noise-level-aware Framework for PET Image Denoising

no code implementations15 Mar 2022 Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se-In Jang, Kyungsang Kim, Kuang Gong, Quanzheng Li

Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.

Image Denoising SSIM

Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction

no code implementations5 Jan 2022 Siqi Li, Kuang Gong, Ramsey D. Badawi, Edward J. Kim, Jinyi Qi, Guobao Wang

In this paper, we propose an implicit regularization for the kernel method by using a deep coefficient prior, which represents the kernel coefficient image in the PET forward model using a convolutional neural-network.

Image Reconstruction

Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior

no code implementations18 Jun 2021 Kuang Gong, Ciprian Catana, Jinyi Qi, Quanzheng Li

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.


Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network

no code implementations14 Sep 2020 Jianan Cui, Kuang Gong, Paul Han, Huafeng Liu, Quanzheng Li

After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.

SSIM Super-Resolution

Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network

no code implementations13 Sep 2020 Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Jianan Cui, Zhifang Wu, Huafeng Liu, Quanzheng Li

Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.


Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples

no code implementations9 Jun 2019 Dufan Wu, Kuang Gong, Kyungsang Kim, Quanzheng Li

In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.

Image Denoising Medical Image Denoising

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

no code implementations17 Dec 2017 Kuang Gong, Jaewon Yang, Kyungsang Kim, Georges El Fakhri, Youngho Seo, Quanzheng Li

With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods.

Image Reconstruction

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

1 code implementation9 Oct 2017 Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Georges El Fakhri, Jinyi Qi, Quanzheng Li

An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool.

Denoising Image Reconstruction

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