Sen Jia

Chinese Academy of Sciences

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Hairong Zheng

Chinese Academy of Sciences

Dong Liang

Shenzhen Institutes of Advanced Technology

Yanjie Zhu

Shenzhen Institutes of Advanced Technology

Jing Cheng

Quanzhou Normal University

Haifeng Wang

Shenzhen Institutes of Advanced Technology

Leslie Ying

University at Buffalo, State University of New York

Zhuo‐Xu Cui

Shenzhen Institutes of Advanced Technology

Xin Liu

Yangzhou University

Ziwen Ke

Shanghai Jiao Tong University

Ye Li

Shenzhen Institutes of Advanced Technology

Cooperative Institutions

Chinese Academy of Sciences

Shenzhen Institutes of Advanced Technology

University of Chinese Academy of Sciences

Shenzhen University

Sun Yat-sen University

Shanghai Jiao Tong University

United Imaging Healthcare (China)

Huazhong University of Science and Technology

Chinese Academy of Medical Sciences & Peking Union Medical College

Central South University

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K-UNN: k-space interpolation with untrained neural network

Medical Image Analysis (2023)

Zhuo‐Xu Cui Sen Jia Chentao Cao Qingyong Zhu Congcong Liu

Interpolation

Smoothness

10.1016/j.media.2023.102877

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Citations (8)

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion

IEEE Transactions on Medical Imaging (2024)

Zhuo‐Xu Cui Congcong Liu Xiaohong Fan Chentao Cao Jing Cheng

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data. Broadly, this insight drives a shift in the generation paradigm from random noise to a more deterministic approach grounded in the existing LF k-space data. Building on this, we first establish a relationship between the interpolation of HF k-space data from LF k-space data and the reverse heat diffusion process, providing a fundamental framework for designing diffusion models that generate missing HF data. To further improve reconstruction accuracy, we integrate a traditional physics-informed k-space interpolation model into our diffusion framework as a data fidelity term. Experimental validation using publicly available datasets demonstrates that our approach significantly surpasses traditional k-space interpolation methods, deep learning-based k-space interpolation techniques, and conventional diffusion models, particularly in HF regions. Finally, we assess the generalization performance of our model across various out-of-distribution datasets. Our code are available at https://github.com/ZhuoxuCui/Heat-Diffusion.

Interpolation

10.1109/tmi.2024.3462988

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Citations (1)

SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging

arXiv (Cornell University) (2021)

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

Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution trade-off: (1) $k$-space undersampling with high-resolution acquisition, and (2) a pipeline of lower resolution image reconstruction and image super-resolution. However, these approaches either have limited performance at certain high acceleration factor or suffer from the error accumulation of two-step structure. In this paper, we combine the idea of MR reconstruction and image super-resolution, and work on recovering HR images from low-resolution under-sampled $k$-space data directly. Particularly, the SR-involved reconstruction can be formulated as a variational problem, and a learnable network unrolled from its solution algorithm is proposed. A discriminator was introduced to enhance the detail refining performance. Experiment results using in-vivo HR multi-coil brain data indicate that the proposed SRR-Net is capable of recovering high-resolution brain images with both good visual quality and perceptual quality.

Net (polyhedron)

10.48550/arxiv.2104.05901

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Citations (3)

T1rho Fractional-order Relaxation of Human Articular Cartilage

arXiv (Cornell University) (2019)

Lixian Zou Haifeng Wang Yanjie Zhu Yuanyuan Liu Jing Cheng

T1rho imaging is a promising non-invasive diagnostic tool for early detection of articular cartilage degeneration. A mono-exponential model is normally used to describe the T1rho relaxation process. However, mono-exponentials may not adequately to describe NMR relaxation in complex, heterogeneous, and anisotropic materials, such as articular cartilage. Fractional-order models have been used successfully to describe complex relaxation phenomena in the laboratory frame in cartilage matrix components. In this paper, we develop a time-fractional order (T-FACT) model for T1rho fitting in human articular cartilage. Representative results demonstrate that the proposed method is able to fit the experimental data with smaller root mean squared error than the one from conventional mono-exponential relaxation model in human articular cartilage.

T2 relaxation

10.48550/arxiv.1905.03937

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Deep Manifold Learning for Dynamic MR Imaging

IEEE Transactions on Computational Imaging (2021)

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

Recently, low-dimensional manifold regularization has been recognized as a competitive method for accelerated cardiac MRI, due to its ability to capture temporal correlations. However, existing methods have not been performed with the nonlinear structure of an underlying manifold. In this paper, we propose a deep learning method in an unrolling manner for accelerated cardiac MRI on a low-dimensional manifold. Specifically, a fixed low-rank tensor (Riemannian) manifold is chosen to capture the strong temporal correlations of dynamic signals; the reconstruction problem is modeled as a CS-based optimization problem on this manifold. Following the manifold structure, a Riemannian gradient descent (RGD) method is adopted to solve this problem. Finally, the RGD algorithm is unrolled into a neural network, called Manifold-Net, on the manifold to avoid the long computation time and the challenging parameter selection. The experimental results at high accelerations demonstrate that the proposed method can obtain improved reconstruction compared with three conventional methods (k-t SLR, SToRM and k-t MLSD) and three state-of-the-art deep learning-based methods (DC-CNN, CRNN, and SLR-Net). To our knowledge, this work represents the first study to unroll the iterative optimization procedure into neural networks on manifolds. Moreover, the designed Manifold-Net provides a new mechanism for low-rank priors in dynamic MRI and should also prove useful for fast reconstruction in other dynamic imaging problems.

Manifold (fluid mechanics)

Manifold alignment

Riemannian manifold

Regularization

10.1109/tci.2021.3131564

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Citations (22)

Super-resolution MR Vessel Wall Images Using deep learning

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)

Wenjing Xu Sen Jia Qing Zhu Yikang Li Hongying Zhang

To develop a super-resolution method based on the 3D high-resolution MR vessel wall images for generating high-resolution images from low-resolution, a 3D complex-valued super resolution (CVSR) neural network was proposed, which maintained complex algebraic structure of the original acquired images. CVSR was trained on 20 pairs of data sets and tested on 5 pairs. Ground truth with 0.44 mm were compared with Fourier interpolation method, EDSR with two real-valued channels and CVSR. Evaluations were performed using structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and error map quality metrics. The CVSR achieved the best performance when compared with the other methods.

Peak signal-to-noise ratio

Interpolation

Similarity (geometry)

Ground truth

10.58530/2022/4716

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Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging

Bioengineering (2023)

Xu Wei Sen Jia Zhuo‐Xu Cui Qingyong Zhu Xin Liu

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

k-space

Feature (linguistics)

10.3390/bioengineering10091107

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Citations (1)

Accelerating MR Imaging via Deep Chambolle-Pock Network

Haifeng Wang Jing Cheng Sen Jia Zhilang Qiu Caiyun Shi

Compressed sensing (CS) has been introduced to accelerate data acquisition in MR Imaging. However, CS-MRI methods suffer from detail loss with large acceleration and complicated parameter selection. To address the limitations of existing CS-MRI methods, a model-driven MR reconstruction is proposed that trains a deep network, named CP-net, which is derived from the Chambolle-Pock algorithm to reconstruct the in vivo MR images of human brains from highly undersampled complex k-space data acquired on different types of MR scanners. The proposed deep network can learn the proximal operator and parameters among the Chambolle-Pock algorithm. All of the experiments show that the proposed CP-net achieves more accurate MR reconstruction results, outperforming state-of-the-art methods across various quantitative metrics.

Net (polyhedron)

10.1109/embc.2019.8857141

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Citations (10)

Erratum to: Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning (Magn Reson Med. 2024;92:496‐518)

Magnetic Resonance in Medicine (2024)

Shanshan Wang Ruoyou Wu Sen Jia Alou Diakite Cheng Li

Due to a misunderstanding of a vendor approach to their reconstruction algorithm, the authors wish to remove the following sentence in the fifth paragraph of Section 4.4: "GE and Siemens solutions are applied to DICOM data after parallel imaging (PI) reconstruction, whereas Philips and UIH solutions attempt to build upon traditional CSPI and iterative algorithms to provide interpretability, and to fully utilize the domain specific experience and knowledge."

10.1002/mrm.30204

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Citations (0)

Real-time volumetric MR thermometry using 3D echo-shifted sequence under an open source reconstruction platform

Magnetic Resonance Imaging (2020)

Rui Jiang Sen Jia Yangzi Qiao Qiaoyan Chen Jianhong Wen

The aim of this work is to implement real-time 3D MR thermometry for high intensity focused ultrasound (HIFU) monitoring.Volumetric MR thermometry was implemented based on a 3D echo-shifted sequence with short TR to improve temperature sensitivity. The 3D acquisition was accelerated in two phase encoding directions with controlled aliasing in volumetric parallel imaging (CAIPIRINHA). Image reconstruction was run in an open source reconstruction platform (Gadgetron).Phantom experiments showed the proposed volumetric thermometry was comparable to the fiber optical thermometer. In-vivo animal experiments in rabbit thigh showed that the temperature error before and after 4× acceleration was less than 0.65 °C. Finally, real-time 3D thermometry with temporal resolution ~3 s and spatial resolution 2 × 2 × 5 mm3 (spatial coverage 192 × 192 × 80 mm3) was achieved with Gadgetron reconstruction.Real-time temperature monitoring was achieved in-vivo by using parallel imaging accelerated 3D echo-shifted sequence with Gadgetron reconstruction.

Aliasing

Echo (communications protocol)

Temporal resolution

Thermometer

10.1016/j.mri.2020.04.001

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Citations (10)