no code implementations • 9 May 2024 • Pinhuang Tan, Mengxiao Geng, Jingya Lu, Liu Shi, Bin Huang, Qiegen Liu
Through precise adjustments in diffusion model, it is capable of extracting diverse noise distribution, furthering the understanding of the overall structure of images, and aiding the fully sampled model in recovering image information more effec-tively.
no code implementations • 9 May 2024 • Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu, Qiqing Liu, Dong Liang, Qiegen Liu
Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently.
no code implementations • 5 Feb 2024 • Shanshan Wang, Ruoyou Wu, Sen Jia, Alou Diakite, Cheng Li, Qiegen Liu, Leslie Ying
The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods.
no code implementations • 3 Jan 2024 • Wenxin Fan, Jian Cheng, Cheng Li, Xinrui Ma, Jing Yang, Juan Zou, Ruoyou Wu, Qiegen Liu, Shanshan Wang
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI).
no code implementations • 1 Dec 2023 • Yuxia Huang, Zhonghui Wu, Xiaoling Xu, Minghui Zhang, Shanshan Wang, Qiegen Liu
After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image.
no code implementations • 1 Nov 2023 • Bohui Shen, Wei zhang, Xubiao Liu, Pengfei Yu, Shirui Jiang, Xinchong Shi, Xiangsong Zhang, Xiaoyu Zhou, Weirui Zhang, Bingxuan Li, Qiegen Liu
Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data.
1 code implementation • 3 Oct 2023 • Yu Guan, Bohui Shen, Xinchong Shi, Xiangsong Zhang, Bingxuan Li, Qiegen Liu
Perceptual analysis and quantitative evaluations illustrate that the invertible network for PET AC outperforms other existing AC models, which demonstrates the potential of the proposed method and the feasibility of achieving brain PET AC without CT.
1 code implementation • 2 Sep 2023 • Yu Guan, Chuanming Yu, Shiyu Lu, Zhuoxu Cui, Dong Liang, Qiegen Liu
In this study, leveraging a combination of the properties of k-space data and the diffusion process, our novel scheme focuses on mining the multi-frequency prior with different strategies to pre-serve fine texture details in the reconstructed image.
1 code implementation • 30 Aug 2023 • Kai Xu, Shiyu Lu, Bin Huang, Weiwen Wu, Qiegen Liu
Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods.
no code implementations • 30 Aug 2023 • Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan, Chentao Cao, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Yihang Zhou, Haifeng Wang, Yanjie Zhu, Jianping Zhang, Qiegen Liu, Dong Liang
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
no code implementations • 15 Apr 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Qiegen Liu, Hairong Zheng, Shanshan Wang
However, high heterogeneity exists in the data from different centers, and existing federated learning methods tend to use average aggregation methods to combine the client's information, which limits the performance and generalization capability of the trained models.
no code implementations • 15 Dec 2022 • Chuanming Yu, Yu Guan, Ziwen Ke, Dong Liang, Qiegen Liu
Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements.
no code implementations • 11 Dec 2022 • Wei zhang, Zengwei Xiao, Hui Tao, Minghui Zhang, Xiaoling Xu, Qiegen Liu
Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in high-dimensional generation.
2 code implementations • 7 Dec 2022 • Bin Huang, Liu Zhang, Shiyu Lu, Boyu Lin, Weiwen Wu, Qiegen Liu
Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction.
1 code implementation • 25 Nov 2022 • Bing Guan, Cailian Yang, Liu Zhang, Shanzhou Niu, Minghui Zhang, Yuhao Wang, Weiwen Wu, Qiegen Liu
When the number of projection view changes, the DL network should be retrained with updated sparse-view/full-view CT image pairs.
1 code implementation • 25 Nov 2022 • Zihao Li, CHUNHUA WU, Shenglin Wu, Wenbo Wan, Yuhao Wang, Qiegen Liu
To better apply the score-based generative model to learn the internal statistical distribution within patches, the large-scale Hankel matrices are finally folded into the higher dimensional tensors for prior learning.
2 code implementations • 15 Aug 2022 • Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.
1 code implementation • 8 May 2022 • Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Pengwen Zhu, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).
1 code implementation • 5 Apr 2022 • Hui Tao, Haifeng Wang, Shanshan Wang, Dong Liang, Xiaoling Xu, Qiegen Liu
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology.
1 code implementation • 21 Mar 2022 • Zongjiang Tu, Chen Jiang, Yu Guan, Shanshan Wang, Jijun Liu, Qiegen Liu, Dong Liang
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.
no code implementations • 18 Mar 2022 • Weijian Huang, Cheng Li, Wenxin Fan, Yongjin Zhou, Qiegen Liu, Hairong Zheng, Shanshan Wang
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction.
1 code implementation • 3 Feb 2022 • Shanshan Wang, Ruoyou Wu, Cheng Li, Juan Zou, Ziyao Zhang, Qiegen Liu, Yan Xi, Hairong Zheng
However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited.
1 code implementation • 25 Jan 2022 • Wanqing Zhu, Bing Guan, Shanshan Wang, Minghui Zhang, Qiegen Liu
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions.
1 code implementation • 19 Jan 2022 • Xianghao Liao, Shanshan Wang, Lanlan Tu, Yuhao Wang, Dong Liang, Qiegen Liu
Additionally, its performance is not susceptible to different number of virtual coils.
no code implementations • 19 Jan 2022 • Cailian Yang, Xianghao Liao, Yuhao Wang, Minghui Zhang, Qiegen Liu
Two main components are incorporated into the network design, namely variable augmentation technology and sum of squares (SOS) objective function.
1 code implementation • 26 Sep 2021 • Chen Hu, Cheng Li, Haifeng Wang, Qiegen Liu, Hairong Zheng, Shanshan Wang
Specifically, during model optimization, two subsets are constructed by randomly selecting part of k-space data from the undersampled data and then fed into two parallel reconstruction networks to perform information recovery.
1 code implementation • 7 Sep 2021 • Yu Guan, Zongjiang Tu, Shanshan Wang, Qiegen Liu, Yuhao Wang, Dong Liang
In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image.
1 code implementation • 2 Sep 2021 • Yuhao Wang, Ruirui Liu, Zihao Li, Cailian Yang, Qiegen Liu
As an effective way to integrate the information contained in multiple medical images under different modalities, medical image synthesis and fusion have emerged in various clinical applications such as disease diagnosis and treatment planning.
1 code implementation • 14 Aug 2021 • Kai Hong, CHUNHUA WU, Cailian Yang, Minghui Zhang, Yancheng Lu, Yuhao Wang, Qiegen Liu
This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks.
4 code implementations • 9 Jul 2021 • Jin Li, Wanyun Li, Zichen Xu, Yuhao Wang, Qiegen Liu
Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples.
6 code implementations • 28 Dec 2020 • Kai Hong, Jin Li, Wanyun Li, Cailian Yang, Minghui Zhang, Yuhao Wang, Qiegen Liu
Furthermore, the joint intensity-gradient constraint in data-fidelity term is proposed to limit the degree of freedom within generative model at the iterative colorization stage, and it is conducive to edge-preserving.
no code implementations • 15 Dec 2020 • Shanshan Wang, Taohui Xiao, Qiegen Liu, Hairong Zheng
Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed".
5 code implementations • 27 Sep 2020 • Zhuonan He, Yikun Zhang, Yu Guan, Shanzhou Niu, Yi Zhang, Yang Chen, Qiegen Liu
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications.
5 code implementations • 14 Aug 2020 • Cong Quan, Jinjie Zhou, Yuanzheng Zhu, Yang Chen, Shan-Shan Wang, Dong Liang, Qiegen Liu
Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.
1 code implementation • 23 Oct 2019 • Zhuonan He, Jinjie Zhou, Dong Liang, Yuhao Wang, Qiegen Liu
Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging.
2 code implementations • 24 Sep 2019 • Yiling Liu, Qiegen Liu, Minghui Zhang, Qingxin Yang, Shan-Shan Wang, Dong Liang
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.
1 code implementation • 3 Sep 2019 • Siyuan Wang, Junjie Lv, Yuanyuan Hu, Dong Liang, Minghui Zhang, Qiegen Liu
At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).
no code implementations • 24 Aug 2019 • Shan-Shan Wang, Yanxia Chen, Taohui Xiao, Ziwen Ke, Qiegen Liu, Hairong Zheng
In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.
no code implementations • 6 Aug 2019 • Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shan-Shan Wang
Three main contributions have been made: a de-aliasing reconstruction model was proposed to accelerate parallel MR imaging with deep learning exploring both spatial redundancy and multi-coil correlations; a split Bregman iteration algorithm was developed to solve the model efficiently; and unlike most existing parallel imaging methods which rely on the accuracy of the estimated multi-coil sensitivity, the proposed method can perform parallel reconstruction from undersampled data without explicit sensitivity calculation.
1 code implementation • 16 Jul 2019 • Kehan Qi, Hao Yang, Cheng Li, Zaiyi Liu, Meiyun Wang, Qiegen Liu, Shan-Shan Wang
Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks.
no code implementations • 19 Jun 2019 • Jing Cheng, Haifeng Wang, Yanjie Zhu, Qiegen Liu, Qiyang Zhang, Ting Su, Jianwei Chen, Yongshuai Ge, Zhanli Hu, Xin Liu, Hairong Zheng, Leslie Ying, Dong Liang
Usually, acquiring less data is a direct but important strategy to address these issues.
no code implementations • 18 Jan 2019 • Ziwen Ke, Shan-Shan Wang, Huitao Cheng, Leslie Ying, Qiegen Liu, Hairong Zheng, Dong Liang
In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN.
no code implementations • 26 Oct 2018 • Wenzhao Zhao, Qiegen Liu, Yisong Lv, Binjie Qin
For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures.