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Yuan T, Yang J, Chi J, Yu T, Liu F. A cross-domain complex convolution neural network for undersampled magnetic resonance image reconstruction. Magn Reson Imaging 2024; 108:86-97. [PMID: 38331053 DOI: 10.1016/j.mri.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
To introduce a new cross-domain complex convolution neural network for accurate MR image reconstruction from undersampled k-space data. Most reconstruction methods utilize neural networks or cascade neural networks in either the image domain and/or the k-space domain. However, these methods encounter several challenges: 1) Applying neural networks directly in the k-space domain is suboptimal for feature extraction; 2) Classic image-domain networks have difficulty in fully extracting texture features; and 3) Existing cross-domain methods still face challenges in extracting and fusing features from both image and k-space domains simultaneously. In this work, we propose a novel deep-learning-based 2-D single-coil complex-valued MR reconstruction network termed TEID-Net. TEID-Net integrates three modules: 1) TE-Net, an image-domain-based sub-network designed to enhance contrast in input features by incorporating a Texture Enhancement Module; 2) ID-Net, an intermediate-domain sub-network tailored to operate in the image-Fourier space, with the specific goal of reducing aliasing artifacts realized by leveraging the superior incoherence property of the decoupled one-dimensional signals; and 3) TEID-Net, a cross-domain reconstruction network in which ID-Nets and TE-Nets are combined and cascaded to boost the quality of image reconstruction further. Extensive experiments have been conducted on the fastMRI and Calgary-Campinas datasets. Results demonstrate the effectiveness of the proposed TEID-Net in mitigating undersampling-induced artifacts and producing high-quality image reconstructions, outperforming several state-of-the-art methods while utilizing fewer network parameters. The cross-domain TEID-Net excels in restoring tissue structures and intricate texture details. The results illustrate that TEID-Net is particularly well-suited for regular Cartesian undersampling scenarios.
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Affiliation(s)
- Tengfei Yuan
- College of Electronics and Information, Qingdao University, Qingdao, Shandong, China
| | - Jie Yang
- College of Mechanical and Electrical Engineering, Qingdao University, Qingdao, Shandong, China
| | - Jieru Chi
- College of Electronics and Information, Qingdao University, Qingdao, Shandong, China.
| | - Teng Yu
- College of Electronics and Information, Qingdao University, Qingdao, Shandong, China
| | - Feng Liu
- School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Brisbane, Australia
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Ni M, He M, Yang Y, Wen X, Zhao Y, Gao L, Yan R, Xu J, Zhang Y, Chen W, Jiang C, Li Y, Zhao Q, Wu P, Li C, Qu J, Yuan H. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective. Eur Radiol 2024; 34:3046-3058. [PMID: 37932390 DOI: 10.1007/s00330-023-10368-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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Affiliation(s)
- Ming Ni
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Miao He
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Yuxin Yang
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Xiaoyi Wen
- Institute of Statistics and Big Data, Renmin University of China, Beijing, People's Republic of China
| | - Yuqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Lixiang Gao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Ruixin Yan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yarui Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Wen Chen
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Chenyu Jiang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yali Li
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Peng Wu
- United Imaging Healthcare Co, Shanghai, People's Republic of China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China.
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
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Jiang N, Zhang Y, Li Q, Fu X, Fang D. A cardiac MRI motion artifact reduction method based on edge enhancement network. Phys Med Biol 2024; 69:095004. [PMID: 38537303 DOI: 10.1088/1361-6560/ad3884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 03/26/2024] [Indexed: 04/16/2024]
Abstract
Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges.
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Affiliation(s)
- Nanhe Jiang
- School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China
| | - Yucun Zhang
- School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China
| | - Qun Li
- School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China
| | - Xianbin Fu
- Hebei University of Environmental Engineering, Qinhuangdao, 066102, Hebei, People's Republic of China
| | - Dongqing Fang
- Capital Aerospace Machinery Co, Ltd, Fengtai, 100076, Beijing, People's Republic of China
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Yan Y, Yang T, Jiao C, Yang A, Miao J. IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction. Phys Med Biol 2024; 69:085005. [PMID: 38479022 DOI: 10.1088/1361-6560/ad33b4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Objective.Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image domain reconstruction with conventional CNN-based structures by full supervision. It ignores the prior information from reference contrast images in other sparse domains and requires fully sampled target contrast data. In addition, because of the limited receptive field, conventional CNN-based networks are difficult to build a high-quality non-local dependency.Approach.In the paper, we propose an Image-Wavelet domain ConvNeXt-based network (IWNeXt) for self-supervised MC MRI reconstruction. Firstly, INeXt and WNeXt based on ConvNeXt reconstruct undersampled target contrast data in the image domain and refine the initial reconstructed result in the wavelet domain respectively. To generate more tissue details in the refinement stage, reference contrast wavelet sub-bands are used as additional supplementary information for wavelet domain reconstruction. Then we design a novel attention ConvNeXt block for feature extraction, which can capture the non-local information of the MC image. Finally, the cross-domain consistency loss is designed for self-supervised learning. Especially, the frequency domain consistency loss deduces the non-sampled data, while the image and wavelet domain consistency loss retain more high-frequency information in the final reconstruction.Main results.Numerous experiments are conducted on the HCP dataset and the M4Raw dataset with different sampling trajectories. Compared with DuDoRNet, our model improves by 1.651 dB in the peak signal-to-noise ratio.Significance.IWNeXt is a potential cross-domain method that can enhance the accuracy of MC MRI reconstruction and reduce reliance on fully sampled target contrast images.
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Affiliation(s)
- Yanghui Yan
- School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, People's Republic of China
| | - Tiejun Yang
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, People's Republic of China
- Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Grain Photoelectric Detection and Control (HAUT), Zhengzhou, Henan, People's Republic of China
| | - Chunxia Jiao
- School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, People's Republic of China
| | - Aolin Yang
- School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, People's Republic of China
| | - Jianyu Miao
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, People's Republic of China
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Wang B, Lian Y, Xiong X, Zhou H, Liu Z, Zhou X. DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction. Magn Reson Imaging 2024; 107:69-79. [PMID: 38237693 DOI: 10.1016/j.mri.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/26/2023] [Accepted: 01/14/2024] [Indexed: 01/22/2024]
Abstract
Current challenges in Magnetic Resonance Imaging (MRI) include long acquisition times and motion artifacts. To address these issues, under-sampled k-space acquisition has gained popularity as a fast imaging method. However, recovering fine details from under-sampled data remains challenging. In this study, we introduce a pioneering deep learning approach, namely DCT-Net, designed for dual-domain MRI reconstruction. DCT-Net seamlessly integrates information from the image domain (IRM) and frequency domain (FRM), utilizing a novel Cross Attention Block (CAB) and Fusion Attention Block (FAB). These innovative blocks enable precise feature extraction and adaptive fusion across both domains, resulting in a significant enhancement of the reconstructed image quality. The adaptive interaction and fusion mechanisms of CAB and FAB contribute to the method's effectiveness in capturing distinctive features and optimizing image reconstruction. Comprehensive ablation studies have been conducted to assess the contributions of these modules to reconstruction quality and accuracy. Experimental results on the FastMRI (2023) and Calgary-Campinas datasets (2021) demonstrate the superiority of our MRI reconstruction framework over other typical methods (most are illustrated in 2023 or 2022) in both qualitative and quantitative evaluations. This holds for knee and brain datasets under 4× and 8× accelerated imaging scenarios.
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Affiliation(s)
- Bin Wang
- National Institute of Metrology, Beijing 100029, China; Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation, Beijing 100029, China; School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Yusheng Lian
- School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Xingchuang Xiong
- National Institute of Metrology, Beijing 100029, China; Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation, Beijing 100029, China.
| | - Han Zhou
- School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Zilong Liu
- National Institute of Metrology, Beijing 100029, China; Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation, Beijing 100029, China.
| | - Xiaohao Zhou
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
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Avidan N, Freiman M. MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107942. [PMID: 38039921 DOI: 10.1016/j.cmpb.2023.107942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/11/2023] [Accepted: 11/21/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND AND OBJECTIVE High-quality reconstruction of MRI images from under-sampled 'k-space' data, which is in the Fourier domain, is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. Over recent years, a wealth of deep neural network (DNN) methods have emerged, aiming to tackle the complex, ill-posed inverse problem linked to this process. However, their instability against variations in the acquisition process and anatomical distribution exposes a deficiency in the generalization of relevant physical models within these DNN architectures. The goal of our work is to enhance the generalization capabilities of DNN methods for k-space interpolation by introducing 'MA-RECON', an innovative mask-aware DNN architecture and associated training method. METHODS Unlike preceding approaches, our 'MA-RECON' architecture encodes not only the observed data but also the under-sampling mask within the model structure. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem. Therefore, effectively represents the associated inverse problem, akin to the classical compressed sensing approach. RESULTS The benefits of our MA-RECON approach were affirmed through rigorous testing with the widely accessible fastMRI dataset. Compared to standard DNN methods and DNNs trained with under-sampling mask augmentation, our approach demonstrated superior generalization capabilities. This resulted in a considerable improvement in robustness against variations in both the acquisition process and anatomical distribution, especially in regions with pathology. CONCLUSION In conclusion, our mask-aware strategy holds promise for enhancing the generalization capacity and robustness of DNN-based methodologies for MRI reconstruction from undersampled k-space data. Code is available in the following link: https://github.com/nitzanavidan/PD_Recon.
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Affiliation(s)
- Nitzan Avidan
- Faculty of Biomedical Engineering, Technion IIT, Haifa, Israel.
| | - Moti Freiman
- Faculty of Biomedical Engineering, Technion IIT, Haifa, Israel.
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