Ziwen Ke

Shanghai Jiao Tong University

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Dong Liang

Shenzhen Institutes of Advanced Technology

Leslie Ying

University at Buffalo, State University of New York

Yanjie Zhu

Shenzhen Institutes of Advanced Technology

Jing Cheng

Quanzhou Normal University

Hairong Zheng

Chinese Academy of Sciences

Sen Jia

Chinese Academy of Sciences

Zhuo‐Xu Cui

Shenzhen Institutes of Advanced Technology

Wenqi Huang

Technical University of Munich

Shanshan Wang

Shenzhen Institutes of Advanced Technology

Haifeng Wang

Shenzhen Institutes of Advanced Technology

Cooperative Institutions

Institute of Electrical and Electronics Engineers

Signal Processing (United States)

Chinese Academy of Sciences

Shenzhen Institutes of Advanced Technology

University of Chinese Academy of Sciences

Shenzhen University

Shanghai Jiao Tong University

University of Illinois Urbana-Champaign

Stanford University

Sun Yat-sen University

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Deep Learning Enables Superior Photoacoustic Imaging at Ultralow Laser Dosages

Advanced Science (2020)

Huangxuan Zhao Ziwen Ke Fan Yang Ke Li Ningbo Chen

Optical-resolution photoacoustic microscopy (OR-PAM) is an excellent modality for in vivo biomedical imaging as it noninvasively provides high-resolution morphologic and functional information without the need for exogenous contrast agents. However, the high excitation laser dosage, limited imaging speed, and imperfect image quality still hinder the use of OR-PAM in clinical applications. The laser dosage, imaging speed, and image quality are mutually restrained by each other, and thus far, no methods have been proposed to resolve this challenge. Here, a deep learning method called the multitask residual dense network is proposed to overcome this challenge. This method utilizes an innovative strategy of integrating multisupervised learning, dual-channel sample collection, and a reasonable weight distribution. The proposed deep learning method is combined with an application-targeted modified OR-PAM system. Superior images under ultralow laser dosage (32-fold reduced dosage) are obtained for the first time in this study. Using this new technique, a high-quality, high-speed OR-PAM system that meets clinical requirements is now conceivable.

Modality (human–computer interaction)

10.1002/advs.202003097

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Citations (56)

Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

arXiv (Cornell University) (2019)

Dong Liang Jing Cheng Ziwen Ke Leslie Ying

Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learning-based image reconstruction methods for MRI. Three types of deep learning-based approaches are reviewed, the data-driven, model-driven and integrated approaches. The main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in-between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. the discussion may facilitate further development of "optimal" network and performance analysis from a theoretical point of view.

Deep Neural Networks

10.48550/arxiv.1907.11711

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Citations (38)

Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks

IEEE Signal Processing Magazine (2020)

Dong Liang Jing Cheng Ziwen Ke Leslie Ying

Image reconstruction from undersampled k-space data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deep-learning-based image reconstruction methods for MRI. Two types of deep-learningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.

Real-time MRI

10.1109/msp.2019.2950557

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Citations (314)

Deep Manifold Learning for Dynamic MR Imaging

arXiv (Cornell University) (2021)

Ziwen Ke Zhuo‐Xu Cui Wenqi Huang Jing Cheng Sen Jia

Purpose: To develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled measurements. Methods: Cardiac MR image reconstruction is modeled as general compressed sensing (CS) based optimization on a low-rank tensor manifold. The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals. Iterative procedures can be obtained by solving the optimization model on the manifold, including gradient calculation, projection of the gradient to tangent space, and retraction of the tangent space to the manifold. The iterative procedures on the manifold are unrolled to a neural network, dubbed as Manifold-Net. The Manifold-Net is trained using in vivo data with a retrospective electrocardiogram (ECG)-gated segmented bSSFP sequence. Results: Experimental results at high accelerations demonstrate that the proposed method can obtain improved reconstruction compared with a compressed sensing (CS) method k-t SLR and two state-of-the-art deep learning-based methods, DC-CNN and CRNN. Conclusion: This work represents the first study unrolling the optimization on manifolds into neural networks. Specifically, the designed low-rank manifold provides a new technical route for applying low-rank priors in dynamic MR imaging.

Manifold (fluid mechanics)

Manifold alignment

10.48550/arxiv.2104.01102

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Learning Data Consistency and its Application to Dynamic MR Imaging

IEEE Transactions on Medical Imaging (2021)

Jing Cheng Zhuo‐Xu Cui Wenqi Huang Ziwen Ke Leslie Ying

Magnetic resonance (MR) image reconstruction from undersampled k-space data can be formulated as a minimization problem involving data consistency and image prior. Existing deep learning (DL)-based methods for MR reconstruction employ deep networks to exploit the prior information and integrate the prior knowledge into the reconstruction under the explicit constraint of data consistency, without considering the real distribution of the noise. In this work, we propose a new DL-based approach termed Learned DC that implicitly learns the data consistency with deep networks, corresponding to the actual probability distribution of system noise. The data consistency term and the prior knowledge are both embedded in the weights of the networks, which provides an utterly implicit manner of learning reconstruction model. We evaluated the proposed approach with highly undersampled dynamic data, including the dynamic cardiac cine data with up to 24-fold acceleration and dynamic rectum data with the acceleration factor equal to the number of phases. Experimental results demonstrate the superior performance of the Learned DC both quantitatively and qualitatively than the state-of-the-art.

Dynamic data

10.1109/tmi.2021.3096232

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Citations (32)

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

arXiv (Cornell University) (2019)

Shanshan Wang Huitao Cheng Leslie Ying Taohui Xiao Ziwen Ke

This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as labeled data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the k space data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-the-art method also demonstrates that the proposed method can reconstruct the desired MR images more accurately.

Convolution (computer science)

10.48550/arxiv.1906.04359

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Citations (3)

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

arXiv (Cornell University) (2019)

Ziwen Ke Shanshan Wang Huitao Cheng Leslie Ying Qiegen Liu

Dynamic magnetic resonance (MR) imaging has generated great research interest, as it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is still one of the challenges for dynamic MR imaging. Most existing methods reconstruct Dynamic MR images from incomplete k-space data under the guidance of compressed sensing (CS) or low rank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still a certain degree of smooth in the reconstructed images at high acceleration factors. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN. Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion. We further utilize the total variation (TV) loss function, which has the edge enhancement properties, for training the networks.

Dynamic imaging

Dynamic Contrast-Enhanced MRI

Feature (linguistics)

10.48550/arxiv.1901.06111

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Citations (1)

Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI

arXiv (Cornell University) (2021)

Zhuo‐Xu Cui Jing Cheng Qingyong Zhu Yuanyuan Liu Sen Jia

Recently, model-driven deep learning unrolls a certain iterative algorithm of a regularization model into a cascade network by replacing the first-order information (i.e., (sub)gradient or proximal operator) of the regularizer with a network module, which appears more explainable and predictable compared to common data-driven networks. Conversely, in theory, there is not necessarily such a functional regularizer whose first-order information matches the replaced network module, which means the network output may not be covered by the original regularization model. Moreover, up to now, there is also no theory to guarantee the global convergence and robustness (regularity) of unrolled networks under realistic assumptions. To bridge this gap, this paper propose to present a safeguarded methodology on network unrolling. Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model. Furthermore, inspired by the ideal of deep equilibrium models, before backpropagating, we carry out the unrolled iterative network to converge to a fixed point to ensure the convergence. In case the measurement data contains noise, we prove that the proposed network is robust against noisy interference. Finally, numerical experiments show that the proposed network consistently outperforms the state-of-the-art MRI reconstruction methods including traditional regularization methods and other deep learning methods.

Regularization

Robustness

10.48550/arxiv.2112.09891

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Citations (3)

Learned Subspace Model Enables Ultrafast Neonatal Brain MR Imaging

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2023)

Ziwen Ke Yue Guan Yudu Li Yunpeng Zhang Tianyao Wang

Fast imaging is essential in neonatal brain MRI. Deep learning-based methods can provide high acceleration rates but their performance is instable when limited training data are available. Subspace model-based approach could reduce the dimensionality of imaging and improve reconstruction stability. This work presents a novel method to integrate neonate-specific subspace model and model-driven deep learning, making stable and ultrafast neonatal MR imaging possible. The feasibility and potential of the proposed method have been demonstrated using in vivo data from four medical centers, producing very encouraging results. With further development, the proposed method may provide an effective tool for neonatal imaging.

10.58530/2022/4686

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A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems

arXiv (Cornell University) (2019)

Huangxuan Zhao Ziwen Ke Ningbo Chen Ke Li Lidai Wang

Deconvolution is the most commonly used image processing method to remove the blur caused by the point-spread-function (PSF) in optical imaging systems. While this method has been successful in deblurring, it suffers from several disadvantages including being slow, since it takes many iterations, suboptimal, in cases where experimental operator chosen to represent PSF is not optimal. In this paper, we are proposing a deep-learning-based deblurring method applicable to optical microscopic imaging systems. We tested the proposed method in database data, simulated data, and experimental data (include 2D optical microscopic data and 3D photoacoustic microscopic data), all of which showed much improved deblurred results compared to deconvolution. To quantify the improved performance, we compared our results against several deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre-determined experimental operator. Our method has the advantages of simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

Deblurring

Point spread function

Operator (biology)

10.48550/arxiv.1910.03928

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Citations (4)