Search Results for author: Qiegen Liu

Found 20 papers, 12 papers with code

Variable Augmented Network for Invertible MR Coil Compression

no code implementations19 Jan 2022 Xianghao Liao, Shanshan Wang, Lanlan Tu, Yuhao Wang, Dong Liang, Qiegen Liu

VAN-ICC trains invertible network by finding an invertible and bijective function, which can map the original image to the compression image.

Virtual Coil Augmentation Technology for MRI via Deep Learning

no code implementations19 Jan 2022 Cailian Yang, Xianghao Liao, Yuhao Wang, Minghui Zhang, Qiegen Liu

The main feature of our work is utilizing dummy variable technology to expand the channel in both the image and k-space domains.

Image Reconstruction

Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework

1 code implementation26 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.

MRI Reconstruction Self-Supervised Learning

MRI Reconstruction Using Deep Energy-Based Model

1 code implementation7 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.

Image Generation MRI Reconstruction

Variable Augmented Network for Invertible Modality Synthesis-Fusion

1 code implementation2 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.

Image Generation

High-dimensional Assisted Generative Model for Color Image Restoration

1 code implementation14 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.

Demosaicking Denoising

Wavelet Transform-assisted Adaptive Generative Modeling for Colorization

3 code implementations9 Jul 2021 Jin Li, Wanyun Li, Zichen Xu, Yuhao Wang, Qiegen Liu

Unsupervised deep learning has recently demonstrated the promise to produce high-quality samples.

Colorization Denoising

Joint Intensity-Gradient Guided Generative Modeling for Colorization

3 code implementations28 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.

Colorization

Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data

no code implementations15 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".

Image Reconstruction

Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model

5 code implementations27 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.

Homotopic Gradients of Generative Density Priors for MR Image Reconstruction

5 code implementations14 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.

Denoising MRI Reconstruction

Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction

1 code implementation23 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.

Denoising Dictionary Learning

IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI

1 code implementation24 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.

Compressive Sensing Denoising

Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction

1 code implementation3 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).

Compressive Sensing Denoising +1

LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset

no code implementations24 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.

MRI Reconstruction SSIM

Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging

no code implementations6 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.

De-aliasing

CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint

no code implementations18 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.

Texture variation adaptive image denoising with nonlocal PCA

no code implementations26 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.

Image Denoising

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