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Zhao Y, Yi Z, Liu Y, Chen F, Xiao L, Leong ATL, Wu EX. Calibrationless multi-slice Cartesian MRI via orthogonally alternating phase encoding direction and joint low-rank tensor completion. NMR IN BIOMEDICINE 2022; 35:e4695. [PMID: 35032072 DOI: 10.1002/nbm.4695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 10/06/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
We propose a multi-slice acquisition with orthogonally alternating phase encoding (PE) direction and subsequent joint calibrationless reconstruction for accelerated multiple individual 2D slices or multi-slice 2D Cartesian MRI. Specifically, multi-slice multi-channel data are first acquired with random or uniform PE undersampling while orthogonally alternating PE direction between adjacent slices. They are then jointly reconstructed through a recently developed low-rank multi-slice Hankel tensor completion (MS-HTC) approach. The proposed acquisition and reconstruction strategy was evaluated with human brain MR data. It effectively suppressed aliasing artifacts even at high acceleration factor, outperforming the existing MS-HTC approach, where PE direction is the same between adjacent slices. More importantly, the new strategy worked robustly with uniform undersampling or random undersampling without any consecutive central k-space lines. In summary, our proposed multi-slice MRI strategy exploits both coil sensitivity and image content similarities across adjacent slices. Orthogonally alternating PE direction among slices substantially facilitates the low-rank completion process and improves image reconstruction quality. This new strategy is applicable to uniform and random PE undersampling. It can be easily implemented in practice for Cartesian parallel imaging of multiple individual 2D slices without any coil sensitivity calibration.
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Affiliation(s)
- Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Zheyuan Yi
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Yilong Liu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
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Chang Y, Pham HA, Li Z. A dual-interpolator method for improving parallel MRI reconstruction. Magn Reson Imaging 2022; 92:108-119. [PMID: 35772581 DOI: 10.1016/j.mri.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 06/18/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
Autocalibration signal is acquired in the k-space-based parallel MRI reconstruction for estimating interpolation coefficients and reconstructing missing unacquired data. Many ACS lines can suppress aliasing artifacts and noise by covering the low-frequency signal region. However, more ACS lines will delay the data acquisition process and therefore elongate the scan time. Furthermore, a single interpolator is often used for recovering missing k-space data, and model error may exist if the single interpolator size is not selected appropriately. In this work, based on the idea of the disagreement-based semi-supervised learning, a dual-interpolator strategy is proposed to collaboratively reconstruct missing k-space data. Two interpolators with different sizes are alternatively applied to estimate and re-estimate missing data in k-space. The disagreement between two interpolators is converged and real missing values are co-estimated from two views. The experimental results show that the proposed method outperforms GRAPPA, SPIRiT, and Nonlinear GRAPPA methods using relatively low number of ACS data, and reduces aliasing artifacts and noise in reconstructed images.
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Affiliation(s)
- Yuchou Chang
- Department of Computer and Information Science Department, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA.
| | | | - Zhiqiang Li
- Barrow Neurological Institute, Phoenix, AZ 85013, USA.
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Kim KH, Seo S, Do WJ, Luu HM, Park SH. Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4572. [PMID: 34114253 DOI: 10.1002/nbm.4572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 06/12/2023]
Abstract
In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.
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Affiliation(s)
- Ki Hwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sunghun Seo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Won-Joon Do
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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Dell'Orso A, Positano V, Arisi G, d'Errico F, Taddei A, Banchi B, De Felice C. OPERA: a novel method to reduce ghost and aliasing artifacts. MAGMA (NEW YORK, N.Y.) 2021; 34:451-467. [PMID: 32785807 DOI: 10.1007/s10334-020-00881-1] [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: 04/21/2020] [Revised: 06/29/2020] [Accepted: 08/03/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE A method for Orthogonal Phase Encoding Reduction of Artifact (OPERA) was developed and tested. MATERIALS AND METHODS Because the position of ghosts and aliasing artifacts is predictable along columns or rows, OPERA combines the intensity values of two images acquired using the same parameters, but with swapped phase-encoding directions, to correct the artifacts. Simulations and phantom experiments were conducted to define the efficacy, robustness, and reproducibility. Clinical validation was performed on a total of 1003 images by comparing the OPERA-corrected images and the corresponding image standard in terms of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR). The method efficacy was also rated using a Likert-type scale response by two experienced independent radiologists using a single-blinded procedure. RESULTS Simulations and phantom experiments demonstrated the robustness and effectiveness of OPERA in reducing artifacts strength. OPERA application did not significantly change the SNR [+ 4.16%; inter-quartile range (IQR): 2.72-5.01%] and CNR (+ 4.30%; IQR: 2.86-6.04%) values. The two radiologists observed a total of 893 original images with artifacts (89.03% of the total images), a reduction in the perceived artifacts of 82.0% and 83.9% (p < 0.0001), and an improvement in the perceived SNR (82.8% and 88.5%; K = 0.714) and perceived CNR (86.9-88.9%; K = 0.722). DISCUSSION The study demonstrated that OPERA reduces MR artifacts and improves the perceived image quality.
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Affiliation(s)
- Andrea Dell'Orso
- Department of Radiology, San Giuseppe Hospital, Empoli AO Toscana Centro, Viale Boccaccio 14, Florence, Italy.
| | | | | | - Francesco d'Errico
- Università di Pisa, Scuola di Ingegneria, Pisa, Italy
- School of Medicine, Yale University, New Haven, CT, USA
| | - Aldo Taddei
- Clinical Department of Radiology, AO Toscana SUD-EST, Poggibonsi General Hospital, Poggibonsi, Italy
| | | | - Claudio De Felice
- AOUS, Neonatal Intensive Care Unit, S.M. Alle Scotte General Hospital, Siena, Italy
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Yuan Z, Jiang M, Wang Y, Wei B, Li Y, Wang P, Menpes-Smith W, Niu Z, Yang G. SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. Front Neuroinform 2020; 14:611666. [PMID: 33324189 PMCID: PMC7726262 DOI: 10.3389/fninf.2020.611666] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/05/2020] [Indexed: 11/17/2022] Open
Abstract
Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.
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Affiliation(s)
- Zhenmou Yuan
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Mingfeng Jiang
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Yaming Wang
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Bo Wei
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Yongming Li
- College of Communication Engineering, Chongqing University, Chongqing, China
| | - Pin Wang
- College of Communication Engineering, Chongqing University, Chongqing, China
| | | | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., London, United Kingdom
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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Chang Y. Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning. Healthc Technol Lett 2019; 6:115-120. [PMID: 31531226 PMCID: PMC6718066 DOI: 10.1049/htl.2018.5031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 01/30/2019] [Accepted: 03/05/2019] [Indexed: 11/20/2022] Open
Abstract
Accurate extraction of vessels plays an important role in assisting diagnosis, treatment, and surgical planning. The Otsu method has been used for extracting vessels in medical images. However, blood vessels in magnetic resonance angiography (MRA) image are considered as a sparse distribution. Pixels on vessels in MRA image are considered as an imbalanced data in classification of vessels and non-vessel tissues. To extract vessels accurately, a novel method using resampling technique and ensemble learning is proposed for solving the imbalanced classification problem. Each pixel is sampled multiple times through multiple local patches within the image. Then, vessel or non-vessel tissue is determined by the ensemble voting mechanism via a p-tile algorithm. Experimental results show that the proposed method is able to outperform the traditional Otsu method by extracting vessels in MRA images more accurately.
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Affiliation(s)
- Yuchou Chang
- Computer Science and Engineering Technology Department, University of Houston-Downtown, Houston 77002, USA
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Wang H, Jia S, Chang Y, Zhu Y, Zou C, Li Y, Liu X, Zheng H, Liang D. Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils. Phys Med Biol 2019; 64:14NT01. [PMID: 31167169 DOI: 10.1088/1361-6560/ab274d] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. This work proposed NL-VCC-GRAPPA to jointly utilize the nonlinear mapped virtual coil and phase conjugated virtual coil to further reduce noise amplification in parallel imaging. In vivo static and dynamic 2D imaging accelerated by uniform undersampling schemes were performed to evaluate the proposed method in terms of visual image quality, root-mean-square-error (RMSE), and geometry factor (g-factor). The effects of acceleration factors, calibration data size and kernel shape on the proposed model were also separately analyzed and discussed. The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.
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Affiliation(s)
- Haifeng Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China. Co-First/Equal Authorship
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8
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Ye JC. Compressed sensing MRI: a review from signal processing perspective. BMC Biomed Eng 2019; 1:8. [PMID: 32903346 PMCID: PMC7412677 DOI: 10.1186/s42490-019-0006-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 02/04/2019] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires multi-dimensional k-space data through 1-D free induction decay or echo signals. This often limits the use of MRI, especially for high resolution or dynamic imaging. Accordingly, many investigators has developed various acceleration techniques to allow fast MR imaging. For the last two decades, one of the most important breakthroughs in this direction is the introduction of compressed sensing (CS) that allows accurate reconstruction from sparsely sampled k-space data. The recent FDA approval of compressed sensing products for clinical scans clearly reflect the maturity of this technology. Therefore, this paper reviews the basic idea of CS and how this technology have been evolved for various MR imaging problems.
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Affiliation(s)
- Jong Chul Ye
- Department of Bio and Brain Engineering, Korea Adv. Inst. of Science & Technology (KAIST), 291 Daehak-ro, Daejeon, Korea
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Chang Y, Wang X, An Z, Wang H. Robotic Path Planning Using A * Algorithm for Automatic Navigation in Magnetic Resonance Angiography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:734-737. [PMID: 30440501 DOI: 10.1109/embc.2018.8512417] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Magnetic resonance navigation (MRN) is an emerging research technique in recent years. The micro/nano robots existing in vessels can be driven by magnetic gradients given by MR scanner. As a non-invasive vascular imaging technique, Magnetic resonance angiography (MRA) is able to provide a vascular network of an anatomy without injection of contrast agent. In order to automatically guide and drive micro/nano robots to target in vascular network, a navigation strategy is desired. In this paper, a novel path planning algorithm based on A* search is proposed. The MRA image is preliminarily processed to extract major vessels. Then, pixel-based A* search algorithm identifies the shortest path between start point and target without human supervision. Experimental results on both of simulation image and MRA image demonstrate that the proposed method is able to accomplish path planning automatically in MRA image. That path can guide the injected micro/nano robots to navigate in the blood vessels.
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Kernel Principal Component Analysis of Coil Compression in Parallel Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:4254189. [PMID: 29849747 PMCID: PMC5933030 DOI: 10.1155/2018/4254189] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 03/07/2018] [Indexed: 11/17/2022]
Abstract
A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.
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Improving GRAPPA reconstruction by frequency discrimination in the ACS lines. Int J Comput Assist Radiol Surg 2015; 10:1699-710. [PMID: 25808257 DOI: 10.1007/s11548-015-1172-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 03/09/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE GRAPPA is a well-known parallel imaging method that recovers the MR magnitude image from aliasing by using a weighted interpolation of the data in k-space. To estimate the optimal reconstruction weights, GRAPPA uses a band along the center of the k-space where the signal is sampled at the Nyquist rate, the so-called autocalibrated (ACS) lines. However, while the subsampled lines usually belong to the medium- to high-frequency areas of the spectrum, the ACS lines include the low-frequency areas around the DC component. The use for estimation and reconstruction of areas of the k-space with very different features may negatively affect the final reconstruction quality. We propose a simple, yet powerful method to eliminate reconstruction artifacts, based on the discrimination of the low-frequency spectrum. METHODS The proposal to improve the estimation of the weights lays on a proper selection of the coefficients within the ACS lines, which advises discarding those points around the DC component. A simple approach is the elimination of a square window in the center of the k-space, although more developed approaches can be used. RESULTS The method is tested using real multiple-coil MRI acquisitions. We empirically show this approach achieves great enhancement rates, while keeping the same complexity of the original GRAPPA and reducing the g-factor. The reconstruction is even more accurate when combined with other reconstruction methods. Improvement rates of 35% are achieved for 32 ACS and acceleration rate of 3. CONCLUSIONS The method proposed highly improves the accuracy of the GRAPPA coefficients and therefore the final image reconstruction. The method is fully compatible with the original GRAPPA formulation and with other optimization methods proposed in literature, and it can be easily implemented into the commercial scanning software.
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Xu L, Guo L, Liu X, Kang L, Chen W, Feng Y. GRAPPA reconstruction with spatially varying calibration of self-constraint. Magn Reson Med 2014; 74:1057-69. [PMID: 25311235 DOI: 10.1002/mrm.25496] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Revised: 09/05/2014] [Accepted: 09/28/2014] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop and evaluate a novel method of generalized auto-calibrating partially parallel acquisition (GRAPPA) with spatially varying calibration of self-constraint for parallel magnetic resonance imaging (MRI) reconstruction. THEORY AND METHODS The conventional GRAPPA independently estimates each missing sample with adjacent acquired data over multiple coils, thereby ignoring correlations inside missing data. Self-constrained methods can exploit correlations inside missing data by imposing linear dependence within full neighborhood kernels and showing improved reconstruction compared with GRAPPA. However, self-constraint kernels are currently calibrated by using auto-calibration signals. Thus, they may be suboptimal for reconstructing outer k-space because of spatially varying correlations. This study proposes a novel GRAPPA method with separate self-constraints (SSC-GRAPPA). In this method, the spatially varying self-constraint coefficients are adaptively calibrated by separately exploiting correlations inside missing and acquired data in the outer k-space. Both phantom and in vivo imaging experiments were conducted with retrospective undersampling to evaluate the performance of the proposed method. RESULTS Compared with GRAPPA and self-constrained GRAPPA, the proposed SSC-GRAPPA generates images with reduced artifacts and noise. CONCLUSION The proposed method provides an effective and efficient approach to improve parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future.
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Affiliation(s)
- Lin Xu
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.,School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Li Guo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xiaoyun Liu
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Lili Kang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wufan Chen
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.,School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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Tamada D, Kose K. Two-dimensional compressed sensing using the cross-sampling approach for low-field MRI systems. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1905-1912. [PMID: 24879645 DOI: 10.1109/tmi.2014.2326864] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
A compressed sensing method using a cross sampling and self-calibrated off-resonance correction is proposed. Estimation of the magnetic field inhomogeneity based on image registration enables the off-resonance correction with no additional radio-frequency pulses or acquisitions. In addition to this advantage, a fast and straightforward calculation was achieved by using the first-order components of the magnetic field inhomogeneity. Imaging experiments using a phantom and a chemically fixed mouse demonstrated practical benefits in improving blurring and artifacts in magnetic resonance images in low field magnetic resonance imaging systems.
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Jiang M, Jin J, Liu F, Yu Y, Xia L, Wang Y, Crozier S. Sparsity-constrained SENSE reconstruction: An efficient implementation using a fast composite splitting algorithm. Magn Reson Imaging 2013; 31:1218-27. [DOI: 10.1016/j.mri.2012.12.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2012] [Revised: 11/27/2012] [Accepted: 12/24/2012] [Indexed: 11/30/2022]
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