Progressive Sub-Band Residual-Learning Network for MR Image Super Resolution

Xue, Xuetong; Wang, Ying; Li, Jie; Jiao, Zhicheng; Ren, Ziqi; Gao, Xinbo

doi:10.1109/jbhi.2019.2945373

Cited by 43 publications

(22 citation statements)

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“…The results obtained in this work indicate the high potential of super resolution (SR) technique [26] in HD-sEMG applications. Advanced SR techniques have been applied to improve resolutions of medical images [27]. HD-sEMG as a type of medical images reflecting muscle activation pattern, is expected to provide more sufficient information using SR algorithms.…”

Section: A Components Employed In the Proposed Methodsmentioning

confidence: 99%

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Jiang

Liu

et al. 2021

IEEE J. Biomed. Health Inform.

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With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometrics tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.76% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications.

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Section: A Components Employed In the Proposed Methodsmentioning

confidence: 99%

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Jiang

Liu

et al. 2021

IEEE J. Biomed. Health Inform.

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“…Most studies have focused on through-plane resolution improvements. Recently, the in-plane resolution improvement methods for 4D MRI have been actively studied [20] – [28] .…”

Section: Related Workmentioning

confidence: 99%

“…The rapid advance of deep learning methods made a variety of the CNN-based SR methods applicable in medical images such as retinal images [8] , [62] and MRIs [21] – [28] . Pham et al [21] , [22] applied the Super-Resolution Convolutional Neural Network (SRCNN) [9] , [10] for brain MRIs.…”

Section: Related Workmentioning

confidence: 99%

“…For 4D MRI, Chun et al proposed a cascaded deep learning method that consists of a denoising autoencoder [67] , downsampling network, and SRGAN [66] . Most of the proposed methods of natural images have been adopted for the medical images with or without minor modifications in the network architecture or a combination of several methods [21] , [23] , [24] , [28] [61] , [62] .…”

Section: Related Workmentioning

confidence: 99%

“…In medical imaging, many deep learning-based frameworks have been introduced for feature extraction, anatomical landmark detection, and segmentation [15] – [17] . Recently, deep learning-based single image SR methods for medical imaging have been actively explored [18] – [28] .…”

Section: Introductionmentioning

confidence: 99%

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Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI

Park

Gach

Kim

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). Results: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$100\times100$ \end{document}

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Synthesizing high‐resolution magnetic resonance imaging using parallel cycle‐consistent generative adversarial networks for fast magnetic resonance imaging

et al. 2021

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The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low-and highresolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self -supervised strategy. Methods and materials:The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated. Results: It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of

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Progressive Sub-Band Residual-Learning Network for MR Image Super Resolution

Cited by 43 publications

References 50 publications

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI

Synthesizing high‐resolution magnetic resonance imaging using parallel cycle‐consistent generative adversarial networks for fast magnetic resonance imaging

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