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Liao C, Yarach U, Cao X, Iyer SS, Wang N, Kim TH, Tian Q, Bilgic B, Kerr AB, Setsompop K. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. Neuroimage 2023; 275:120168. [PMID: 37187364 PMCID: PMC10451786 DOI: 10.1016/j.neuroimage.2023.120168] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/12/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.
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Affiliation(s)
- Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Uten Yarach
- Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Siddharth Srinivasan Iyer
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Computer Engineering, Hongik University, Seoul, South Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Adam B Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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2
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Kebiri H, Canales-Rodríguez EJ, Lajous H, de Dumast P, Girard G, Alemán-Gómez Y, Koob M, Jakab A, Bach Cuadra M. Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain. Front Neurol 2022; 13:827816. [PMID: 35585848 PMCID: PMC9109939 DOI: 10.3389/fneur.2022.827816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.
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Affiliation(s)
- Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Hélène Lajous
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Gabriel Girard
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - András Jakab
- Center for MR Research University Children's Hospital Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Zhang X, Liu H, Wang Z, Dong H, Ge J, Liu Z. Anomaly detection of complex magnetic measurements using structured Hankel low-rank modeling and singular value decomposition. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2022; 93:045107. [PMID: 35489876 DOI: 10.1063/5.0088254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
The magnetic anomalies generated by the ferromagnetic targets are usually buried within uncontrollable interference sources, such as the power frequency and random noises. In particular, the variability of the geomagnetic field and the low signal-to-noise ratio (SNR) of the magnetic anomalies cannot be avoided. In this paper, to improve the performance of magnetic anomaly detection (MAD) with a low SNR, we propose a novel structured low-rank (SLR) decomposition-based MAD method. In addition, a new framework based on the SLR and singular value decomposition (SVD) is constructed, dubbed SLR-SVD, and the corresponding working principle and implemented strategy are elaborated. Through comparing the SLR-SVD with two state-of-the-art methods, including principal component analysis and SVD, the results demonstrate that the proposed SLR-SVD can not only suppress the noise sufficiently, i.e., improving 55.26% approximately of the SNR, but also retain more boundary information of magnetic anomalies, i.e., decreasing approximately 68.05% of the mean squared error and improving approximately 28.47% of the structural similarity index.
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Affiliation(s)
- Xinglin Zhang
- School of Automation, China University of Geosciences, Wuhan 430074, China
| | - Huan Liu
- School of Automation, China University of Geosciences, Wuhan 430074, China
| | - Zehua Wang
- School of Automation, China University of Geosciences, Wuhan 430074, China
| | - Haobin Dong
- School of Automation, China University of Geosciences, Wuhan 430074, China
| | - Jian Ge
- School of Automation, China University of Geosciences, Wuhan 430074, China
| | - Zheng Liu
- School of Engineering, University of British Columbia Okanagan Campus, Kelowna, British Columbia V1V 1V7, Canada
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4
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Dai E, Lee PK, Dong Z, Fu F, Setsompop K, McNab JA. Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:63-74. [PMID: 34383645 PMCID: PMC8799377 DOI: 10.1109/tmi.2021.3104291] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.
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Liu H, Wang X, Zhao C, Wang Z, Ge J, Dong H, Liu Z. A modular magneto-inductive sensor for low vector magnetic field measurements. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:085110. [PMID: 34470370 DOI: 10.1063/5.0063450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 08/07/2021] [Indexed: 06/13/2023]
Abstract
The low magnetic field measurement has been utilized since ancient times in order to find economic resources, to detect magnetic anomalies, etc. In this case, the vector magnetic survey can simultaneously obtain the modulus and direction information of the magnetic field, which can contribute to obtaining more precise information and characteristics of magnetic field resources. This paper is concerned with the potential to exploit the signals of vector magnetic field measurement with a magneto-inductive (MI) sensor. To evaluate the capability of the MI sensor, a test platform is set up and its performance, including the noise floor, the resolution, and the sensitivity, is comprehensively characterized. Furthermore, a comparative geomagnetic observation and magnetic anomaly detection among the proposed MI sensor, a high-precision Overhauser sensor, and a commonly used and accepted commercial MI sensor are conducted. The experimental results identify the capability of the proposed MI sensor in weak magnetic detection.
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Affiliation(s)
- Huan Liu
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Xiaobin Wang
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Changfeng Zhao
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Zehua Wang
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Jian Ge
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Haobin Dong
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Zheng Liu
- School of Engineering, University of British Columbia Okanagan Campus, Kelowna, British Columbia V1V 1V7, Canada
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6
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Cao X, Wang K, Liao C, Zhang Z, Srinivasan Iyer S, Chen Z, Lo WC, Liu H, He H, Setsompop K, Zhong J, Bilgic B. Efficient T 2 mapping with blip-up/down EPI and gSlider-SMS (T 2 -BUDA-gSlider). Magn Reson Med 2021; 86:2064-2075. [PMID: 34046924 DOI: 10.1002/mrm.28872] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. METHODS A T2 blip-up/down EPI acquisition with generalized slice-dithered enhanced resolution (T2 -BUDA-gSlider) is proposed. A RF-encoded multi-slab spin-echo (SE) EPI acquisition with multiple TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and used for T2 quantification. RESULTS In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated. CONCLUSION BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications.
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Affiliation(s)
- Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Kang Wang
- Department of Neurology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Zijing Zhang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Siddharth Srinivasan Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zhifeng Chen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, Massachusetts, USA
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Harvard-MIT Department of Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Harvard-MIT Department of Health Sciences and Technology, Cambridge, Massachusetts, USA
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7
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Liao C, Bilgic B, Tian Q, Stockmann JP, Cao X, Fan Q, Iyer SS, Wang F, Ngamsombat C, Lo WC, Manhard MK, Huang SY, Wald LL, Setsompop K. Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic B 0 shimming. Magn Reson Med 2021; 86:791-803. [PMID: 33748985 DOI: 10.1002/mrm.28748] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 01/10/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE We combine SNR-efficient acquisition and model-based reconstruction strategies with newly available hardware instrumentation to achieve distortion-free in vivo diffusion MRI of the brain at submillimeter-isotropic resolution with high fidelity and sensitivity on a clinical 3T scanner. METHODS We propose blip-up/down acquisition (BUDA) for multishot EPI using interleaved blip-up/blip-down phase encoding and incorporate B0 forward-modeling into structured low-rank reconstruction to enable distortion-free and navigator-free diffusion MRI. We further combine BUDA-EPI with an SNR-efficient simultaneous multislab acquisition (generalized slice-dithered enhanced resolution ["gSlider"]), to achieve high-isotropic-resolution diffusion MRI. To validate gSlider BUDA-EPI, whole-brain diffusion data at 860-μm and 780-μm data sets were acquired. Finally, to improve the conditioning and minimize noise penalty in BUDA reconstruction at very high resolutions where B0 inhomogeneity can have a detrimental effect, the level of B0 inhomogeneity was reduced by incorporating slab-by-slab dynamic shimming with a 32-channel AC/DC coil into the acquisition. Whole-brain 600-μm diffusion data were then acquired with this combined approach of gSlider BUDA-EPI with dynamic shimming. RESULTS The results of 860-μm and 780-μm datasets show high geometry fidelity with gSlider BUDA-EPI. With dynamic shimming, the BUDA reconstruction's noise penalty was further alleviated. This enables whole-brain 600-μm isotropic resolution diffusion imaging with high image quality. CONCLUSIONS The gSlider BUDA-EPI method enables high-quality, distortion-free diffusion imaging across the whole brain at submillimeter resolution, where the use of multicoil dynamic B0 shimming further improves reconstruction performance, which can be particularly useful at very high resolutions.
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Affiliation(s)
- Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Jason P Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Xiaozhi Cao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Siddharth Srinivasan Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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8
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Ge J, Wang W, Dong H, Liu H, Zhang X, Luo W, Zhang H, Zhu J. Modeling and reduction of the gradient effect of a magnetic sensor for an aeromagnetic compensator. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:024502. [PMID: 33648067 DOI: 10.1063/5.0028025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
The traditional aeromagnetic compensation model does not consider the negative effects of the gradient of the geomagnetic background field. Especially in emergency aeromagnetic surveys, this gradient effect is obvious and can reduce the performance of magnetic compensation. To solve this problem, we propose a method of reducing the gradient effect of the magnetic sensor for aeromagnetic compensation. A magnetic gradient model of the Earth's background field is established for the aeromagnetic compensation, and the gradient effect is then suppressed using altitude data. An experimental platform was built, and flight tests were conducted. Comparison results show that the adoption of the proposed method increased the improvement ratio (IR) of our compensator on two flights from 20.4 and 21.3 to 25.9 and 26.0, with the growth rates of the IR, respectively, reaching 27% and 22%.
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Affiliation(s)
- Jian Ge
- School of Automation, China University of Geosciences, Wuhan 430034, China
| | - Wenjie Wang
- School of Automation, China University of Geosciences, Wuhan 430034, China
| | - Haobin Dong
- School of Automation, China University of Geosciences, Wuhan 430034, China
| | - Huan Liu
- School of Automation, China University of Geosciences, Wuhan 430034, China
| | - Xinglin Zhang
- School of Automation, China University of Geosciences, Wuhan 430034, China
| | - Wang Luo
- School of Automation, China University of Geosciences, Wuhan 430034, China
| | - Haiyang Zhang
- Science and Technology on Near-Surface Detection Laboratory, Wuxi 214000, China
| | - Jun Zhu
- Science and Technology on Near-Surface Detection Laboratory, Wuxi 214000, China
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9
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Gao Y, Zhou Z, Han F, Zhong X, Yang Y, Hu P. 3D isotropic resolution diffusion‐prepared magnitude‐stabilized bSSFP imaging with high geometric fidelity at 1.5 Tesla. Med Phys 2020; 47:3511-3519. [DOI: 10.1002/mp.14195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/18/2020] [Accepted: 04/14/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
- Yu Gao
- Department of Radiological Sciences University of California Los Angeles CA USA
- Physics and Biology in Medicine IDP University of California Los Angeles CA USA
| | - Ziwu Zhou
- Department of Radiological Sciences University of California Los Angeles CA USA
| | - Fei Han
- Department of Radiological Sciences University of California Los Angeles CA USA
| | - Xiaodong Zhong
- MR R&D Collaborations Siemens Healthcare Los Angeles CA USA
| | - Yingli Yang
- Physics and Biology in Medicine IDP University of California Los Angeles CA USA
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Peng Hu
- Department of Radiological Sciences University of California Los Angeles CA USA
- Physics and Biology in Medicine IDP University of California Los Angeles CA USA
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10
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Mani MP, Aggarwal HK, Ghosh S, Jacob M. Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:913-916. [PMID: 33574989 DOI: 10.1109/isbi45749.2020.9098593] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high resolution imaging. The proposed reconstruction jointly recovers all the diffusion weighted images in a single step from a joint k-q under-sampled acquisition in a parallel MRI setting. We propose the novel use of a pre-trained denoiser as a regularizer in a model-based reconstruction for the recovery of highly under-sampled data. Specifically, we designed the denoiser based on a general diffusion MRI tissue microstructure model for multi-compartmental modeling. By using a wide range of biologically plausible parameter values for the multi-compartmental microstructure model, we simulated diffusion signal that spans the entire microstructure parameter space. A neural network was trained in an unsupervised manner using an autoencoder to learn the diffusion MRI signal subspace. We employed the autoencoder in a model-based reconstruction and show that the autoencoder provides a strong denoising prior to recover the q-space signal. We show reconstruction results on a simulated brain dataset that shows high acceleration capabilities of the proposed method.
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11
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Liu H, Wang H, Bin J, Dong H, Ge J, Liu Z, Yuan Z, Zhu J, Luan X. Efficient noise reduction for the free induction decay signal from a proton precession magnetometer with time-frequency peak filtering. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2020; 91:045101. [PMID: 32357708 DOI: 10.1063/1.5144714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/14/2020] [Indexed: 06/11/2023]
Abstract
The proton precession magnetometer (PPM) is a commonly used device to measure the varying magnetic field. Since the frequency of the PPM sensing free induction decay (FID) signal is proportional to the magnetic field, the signal-to-noise ratio (SNR) is always a critical issue that influences the measurement accuracy severely due to the external interferences such as harmonic noise and random noise. In this study, to boost the SNR of the FID signal, an effective filtering algorithm based on time-frequency peak filtering (TFPF) analyzed with pseudo-Wigner-Ville distribution (PWVD) is proposed. Through pre-treating the collected noisy FID signal with frequency modulation and instantaneous frequency estimation using the peak value of the time-frequency characterization, the embedded noise can be decorrelated and the relative pure FID signal can be detected regardless of the impact of varying noise levels. The superiority of the proposed synaptic noise reduction framework, namely, TFPF-PWVD, was found by comparing it with state-of-the-art approaches under the same conditions. The results illustrated that even though in a strong-noisy scenario, the proposed TFPF-PWVD based approach still achieved the best SNR for the yielded sensing FID and the minimum standard deviation for the observed magnetic field data, which can enhance the geomagnetic measuring performance of a PPM.
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Affiliation(s)
- Huan Liu
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Hongpeng Wang
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Junchi Bin
- School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, British Columbia V1V 1V7, Canada
| | - Haobin Dong
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Jian Ge
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Zheng Liu
- School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, British Columbia V1V 1V7, Canada
| | - Zhiwen Yuan
- Science and Technology on Near-Surface Detection Laboratory, Wuxi, Jiangsu 214035, China
| | - Jun Zhu
- Science and Technology on Near-Surface Detection Laboratory, Wuxi, Jiangsu 214035, China
| | - Xinqun Luan
- Science and Technology on Near-Surface Detection Laboratory, Wuxi, Jiangsu 214035, China
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12
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Ge J, Luo W, Dong H, Liu H, Wang H, Wang W, Yuan Z, Zhu J, Zhang H. Towed Overhauser marine magnetometer for weak magnetic anomaly detection in severe ocean conditions. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2020; 91:035112. [PMID: 32259925 DOI: 10.1063/1.5134929] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 02/24/2020] [Indexed: 06/11/2023]
Abstract
A towed Overhauser marine geomagnetic magnetometer used for weak magnetic anomaly detection in severe ocean conditions is studied to investigate means to reduce the negative effect of dynamic behavior and magnetic noise associated with ocean waves. For the dynamic effect, a continuous polarization workflow is proposed to enhance the free-induction-decay signal, and then, a multi-angle pickup coil and a self-tracking programmable amplifier are used to further reduce the adverse effect caused by uncontrollable changes in the towfish attitude on the signal quality. Furthermore, to achieve adaptive suppression of magnetic noise in different ocean conditions and areas, a modified adaptive Kalman algorithm is assessed. In addition, an optimized Overhauser sensor and a towfish were developed. Overall, the experimental results show that the sensor can effectively suppress the dynamic effect and magnetic noise. Regarding the magnetic sensitivity, uncertainty and range are 12 pT/Hz1/2@1Hz and 0.21 nT and 20 000 nT-100 000 nT, respectively. Moreover, underwater testing was performed to verify the function and the detection of the magnetic anomaly.
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Affiliation(s)
- J Ge
- School of Automation, China University of Geosciences, Lumo Road, Wuhan 430074, China
| | - W Luo
- School of Automation, China University of Geosciences, Lumo Road, Wuhan 430074, China
| | - H Dong
- School of Automation, China University of Geosciences, Lumo Road, Wuhan 430074, China
| | - H Liu
- School of Automation, China University of Geosciences, Lumo Road, Wuhan 430074, China
| | - H Wang
- School of Automation, China University of Geosciences, Lumo Road, Wuhan 430074, China
| | - W Wang
- School of Automation, China University of Geosciences, Lumo Road, Wuhan 430074, China
| | - Z Yuan
- Science and Technology on Near-Surface Detection Laboratory, Tonghuixi Road, Wuxi 214035, China
| | - J Zhu
- Science and Technology on Near-Surface Detection Laboratory, Tonghuixi Road, Wuxi 214035, China
| | - H Zhang
- Science and Technology on Near-Surface Detection Laboratory, Tonghuixi Road, Wuxi 214035, China
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13
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Wang H, Zhou Y, Su S, Hu Z, Liao J, Chang Y. Adaptive Volterra Filter for Parallel MRI Reconstruction. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2019; 2019:34. [DOI: 10.1186/s13634-019-0633-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 07/08/2019] [Indexed: 09/12/2023]
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14
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Ge J, Wang W, Dong H, Liu H, Li H, Luo W, Wang H, Yuan Z, Zhang H, Zhu J. Suppression of the negative effect of abnormal data based on the Hough transform and application to the magnetic compensation of airborne optically-pumped magnetometer data. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2019; 90:115103. [PMID: 31779374 DOI: 10.1063/1.5121771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 10/11/2019] [Indexed: 06/10/2023]
Abstract
A magnetic sensor of the optically pumped magnetometer may enter a dead zone during an aeromagnetic survey, resulting in unavoidable abnormal data and seriously affecting the effect of aeromagnetic compensation. We propose a fast discrimination and culling method based on the Hough transform to prevent abnormal data from participating in the coefficient estimation. In the proposed method, the parameter space is partitioned into small buckets and the most frequently passed region of sinusoidal curves is detected to eliminate abnormal data. Although the conventional method performs similarly to the proposed method when there is only 1% abnormal data, it is theoretically shown that the proposed method has a better goodness of fit of 0.9518, compared with a value of 0.1956 for the conventional method, in the presence of 45% abnormal data. Furthermore, we construct an experimental platform and conduct a flight test in which the proposed method has an improvement ratio of 4.11 compared with a value of 0.34 for the conventional method.
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Affiliation(s)
- Jian Ge
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Wenjie Wang
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Haobin Dong
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Huan Liu
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Han Li
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Wang Luo
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Hongpeng Wang
- School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
| | - Zhiwen Yuan
- Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China
| | - Haiyang Zhang
- Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China
| | - Jun Zhu
- Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China
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15
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Liao C, Stockmann J, Tian Q, Bilgic B, Arango NS, Manhard MK, Huang SY, Grissom WA, Wald LL, Setsompop K. High-fidelity, high-isotropic-resolution diffusion imaging through gSlider acquisition with B 1 + and T 1 corrections and integrated ΔB 0 /Rx shim array. Magn Reson Med 2019; 83:56-67. [PMID: 31373048 DOI: 10.1002/mrm.27899] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/10/2019] [Accepted: 06/17/2019] [Indexed: 01/24/2023]
Abstract
PURPOSE B 1 + and T1 corrections and dynamic multicoil shimming approaches were proposed to improve the fidelity of high-isotropic-resolution generalized slice-dithered enhanced resolution (gSlider) diffusion imaging. METHODS An extended reconstruction incorporating B 1 + inhomogeneity and T1 recovery information was developed to mitigate slab-boundary artifacts in short-repetition time (TR) gSlider acquisitions. Slab-by-slab dynamic B0 shimming using a multicoil integrated ΔB0 /Rx shim array and high in-plane acceleration (Rinplane = 4) achieved with virtual-coil GRAPPA were also incorporated into a 1-mm isotropic resolution gSlider acquisition/reconstruction framework to achieve a significant reduction in geometric distortion compared to single-shot echo planar imaging (EPI). RESULTS The slab-boundary artifacts were alleviated by the proposed B 1 + and T1 corrections compared to the standard gSlider reconstruction pipeline for short-TR acquisitions. Dynamic shimming provided >50% reduction in geometric distortion compared to conventional global second-order shimming. One-millimeter isotropic resolution diffusion data show that the typically problematic temporal and frontal lobes of the brain can be imaged with high geometric fidelity using dynamic shimming. CONCLUSIONS The proposed B 1 + and T1 corrections and local-field control substantially improved the fidelity of high-isotropic-resolution diffusion imaging, with reduced slab-boundary artifacts and geometric distortion compared to conventional gSlider acquisition and reconstruction. This enabled high-fidelity whole-brain 1-mm isotropic diffusion imaging with 64 diffusion directions in 20 min using a 3T clinical scanner.
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Affiliation(s)
- Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Jason Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Nicolas S Arango
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - William A Grissom
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
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16
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Phase-matched virtual coil reconstruction for highly accelerated diffusion echo-planar imaging. Neuroimage 2019; 194:291-302. [DOI: 10.1016/j.neuroimage.2019.04.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/26/2019] [Accepted: 04/01/2019] [Indexed: 11/24/2022] Open
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17
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Liu H, Dong H, Ge J, Liu Z, Yuan Z, Zhu J, Zhang H. A fusion of principal component analysis and singular value decomposition based multivariate denoising algorithm for free induction decay transversal data. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2019; 90:035116. [PMID: 30927809 DOI: 10.1063/1.5089582] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Accepted: 03/07/2019] [Indexed: 05/27/2023]
Abstract
The free induction decay (FID) transversal data determines the measurement accuracy of time-dependent geomagnetic fields, whereas the conservation of clean components and removal of noise cannot be easily achieved for this kind of data. Even though numerous techniques have been proven to be effective in improving the signal-to-noise ratio by filtering out frequency bands, how to efficiently reduce noise is still a crucial issue due to several restrictions, e.g., prior information requirement, stationary data assumption. To end this, a new multivariate algorithm based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD), namely, principal component analysis and decomposition (PCAD), was presented. This novel algorithm aims to reduce noise as well as cancel the interference of FID transversal data. Specifically, the PCAD algorithm is able to obtain the dominant principal components of the FID and that of the noise floor by PCA, in which an optimal number of subspaces could be retained via a cumulative percent of variance criterion. Furthermore, the PCA was combined with an SVD filter whose singular values corresponding to the interferences were identified, and then the noise was suppressed by nulling the corresponding singular values, which was able to achieve an optimum trade-off between the preservation of pure FID data and the denoising efficiency. Our proposed PCAD algorithm was compared with the widely used filter methods via extensive experiments on synthetic and real FID transversal data under different noise levels. The results demonstrated that this method can preserve the FID transversal data better and shows a significant improvement in noise suppression.
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Affiliation(s)
- Huan Liu
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Haobin Dong
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Jian Ge
- School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
| | - Zheng Liu
- School of Engineering, University of British Columbia Okanagan Campus, Kelowna, British Columbia V1V 1V7, Canada
| | - Zhiwen Yuan
- Science and Technology on Near-Surface Detection Laboratory, Wuxi, Jiangsu 214035, China
| | - Jun Zhu
- Science and Technology on Near-Surface Detection Laboratory, Wuxi, Jiangsu 214035, China
| | - Haiyang Zhang
- Science and Technology on Near-Surface Detection Laboratory, Wuxi, Jiangsu 214035, China
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18
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Hu Y, Levine EG, Tian Q, Moran CJ, Wang X, Taviani V, Vasanawala S, McNab JA, Daniel BL, Hargreaves BA. Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization. Magn Reson Med 2019; 81:1181-1190. [PMID: 30346058 PMCID: PMC6289606 DOI: 10.1002/mrm.27488] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 07/11/2018] [Accepted: 07/18/2018] [Indexed: 11/12/2022]
Abstract
PURPOSE The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation. METHODS Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. RESULTS The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality. CONCLUSION We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
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Affiliation(s)
- Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Evan G. Levine
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Xiaole Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | | | | | - Jennifer A. McNab
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Bruce L. Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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19
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Hu J, Li M, Dai Y, Geng C, Tong B, Zhou Z, Liang X, Yang W, Zhang B. Combining SENSE and reduced field-of-view for high-resolution diffusion weighted magnetic resonance imaging. Biomed Eng Online 2018; 17:77. [PMID: 29903023 PMCID: PMC6003092 DOI: 10.1186/s12938-018-0511-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 06/05/2018] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND In diffusion-weighted magnetic resonance imaging (DWI) using single-shot echo planar imaging (ss-EPI), both reduced field-of-view (FOV) excitation and sensitivity encoding (SENSE) alone can increase in-plane resolution to some degree. However, when the two techniques are combined to further increase resolution without pronounced geometric distortion, the resulted images are often corrupted by high level of noise and artifact due to the numerical restriction in SENSE. Hence, this study is aimed to provide a reconstruction method to deal with this problem. METHODS The proposed reconstruction method was developed and implemented to deal with the high level of noise and artifact in the combination of reduced FOV imaging and traditional SENSE, in which all the imaging data were considered jointly by incorporating the motion induced phase variations among excitations. The in vivo human spine diffusion images from ten subjects were acquired at 1.5 T and reconstructed using the proposed method, and compared with SENSE magnitude average results for a range of reduction factors in reduced FOV. These images were evaluated by two radiologists using visual scores (considering distortion, noise and artifact levels) from 1 to 10. RESULTS The proposed method was able to reconstruct images with greatly reduced noise and artifact compared to SENSE magnitude average. The mean g-factors were maintained close to 1 along with enhanced signal-to-noise ratio efficiency. The image quality scores of the proposed method were significantly higher (P < 0.01) than SENSE magnitude average for all the evaluated reduction factors. CONCLUSION The proposed method can improve the combination of SENSE and reduced FOV for high-resolution ss-EPI DWI with reduced noise and artifact.
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Affiliation(s)
- Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Ming Li
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, Jiangsu, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Baotong Tong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xue Liang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, Jiangsu, China
| | - Wen Yang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, Jiangsu, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, Jiangsu, China.
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20
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Chen NK, Chang HC, Bilgin A, Bernstein A, Trouard TP. A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI. PLoS One 2018; 13:e0195952. [PMID: 29694400 PMCID: PMC5918820 DOI: 10.1371/journal.pone.0195952] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 04/03/2018] [Indexed: 11/23/2022] Open
Abstract
Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.
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Affiliation(s)
- Nan-kuei Chen
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, United States of America
- Department of Medical Imaging, University of Arizona, Tucson, Arizona, United States of America
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, United States of America
- * E-mail:
| | - Hing-Chiu Chang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong
| | - Ali Bilgin
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, United States of America
- Department of Medical Imaging, University of Arizona, Tucson, Arizona, United States of America
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona, United States of America
- BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America
| | - Adam Bernstein
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, United States of America
| | - Theodore P. Trouard
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, United States of America
- Department of Medical Imaging, University of Arizona, Tucson, Arizona, United States of America
- BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America
- Evelyn F McKnight Brain Institute, University of Arizona, Tucson, Arizona, United States of America
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21
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Cao X, Liao C, Wang Z, Chen Y, Ye H, He H, Zhong J. Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting. Magn Reson Med 2016; 78:1579-1588. [PMID: 27851871 DOI: 10.1002/mrm.26521] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 09/28/2016] [Accepted: 09/29/2016] [Indexed: 11/12/2022]
Abstract
PURPOSE To develop a method for accelerated and robust MR fingerprinting (MRF) with improved image reconstruction and parameter matching processes. THEORY AND METHODS A sliding-window (SW) strategy was applied to MRF, in which signal and dictionary matching was conducted between fingerprints consisting of mixed-contrast image series reconstructed from consecutive data frames segmented by a sliding window, and a precalculated mixed-contrast dictionary. The effectiveness and performance of this new method, dubbed SW-MRF, was evaluated in both phantom and in vivo. Error quantifications were conducted on results obtained with various settings of SW reconstruction parameters. RESULTS Compared with the original MRF strategy, the results of both phantom and in vivo experiments demonstrate that the proposed SW-MRF strategy either provided similar accuracy with reduced acquisition time, or improved accuracy with equal acquisition time. Parametric maps of T1 , T2 , and proton density of comparable quality could be achieved with a two-fold or more reduction in acquisition time. The effect of sliding-window width on dictionary sensitivity was also estimated. CONCLUSION The novel SW-MRF recovers high quality image frames from highly undersampled MRF data, which enables more robust dictionary matching with reduced numbers of data frames. This time efficiency may facilitate MRF applications in time-critical clinical settings. Magn Reson Med 78:1579-1588, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Congyu Liao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhixing Wang
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ying Chen
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Huihui Ye
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
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