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Hu Y, Zhang X, Chen D, Yan Z, Shen X, Yan G, Ou-Yang L, Lin J, Dong J, Qu X. Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-enhanced MRI. IEEE Trans Biomed Eng 2021; 69:229-243. [PMID: 34166181 DOI: 10.1109/tbme.2021.3091881] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast-thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on both the brain tumor DCE and liver DCE show that, at relatively high acceleration factor of fast sampling, lowest reconstruction error and highest image structural similarity are obtained by the proposed method. Besides, the proposed method achieves faster reconstruction for liver datasets and better physiological measures are also obtained on tumor images.
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Li YY, Zhang P, Rashid S, Cheng YJ, Li W, Schapiro W, Gliganic K, Yamashita AM, Grgas M, Haag E, Cao JJ. Real-time exercise stress cardiac MRI with Fourier-series reconstruction from golden-angle radial data. Magn Reson Imaging 2020; 75:89-99. [PMID: 33098934 DOI: 10.1016/j.mri.2020.10.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/30/2020] [Accepted: 10/18/2020] [Indexed: 10/23/2022]
Abstract
Magnetic resonance imaging (MRI) can measure cardiac response to exercise stress for evaluating and managing heart patients in the practice of clinical cardiology. However, exercise stress cardiac MRI have been clinically limited by the ability of available MRI techniques to quantitatively measure fast and unstable cardiac dynamics during exercise. The presented work is to develop a new real-time MRI technique for improved quantitative performance of exercise stress cardiac MRI. This technique seeks to represent real-time cardiac images as a sparse Fourier-series along the time. With golden-angle radial acquisition, parallel imaging and compressed sensing can be integrated into a linear system of equations for resolving Fourier coefficients that are in turn used to generate real-time cardiac images from the Fourier-series representation. Fourier-series reconstruction from golden-angle radial data can effectively address data insufficiency due to MRI speed limitation, providing a real-time approach to exercise stress cardiac MRI. To demonstrate the feasibility, an exercise stress cardiac MRI experiment was run to investigate biventricular response to in-scanner biking exercise in a cohort of sixteen healthy volunteers. It was found that Fourier-series reconstruction from golden-angle radial data effectively detected exercise-induced increase in stroke volume and ejection fraction in a healthy heart. The presented work will improve the applications of exercise stress cardiac MRI in the practice of clinical cardiology.
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Affiliation(s)
- Yu Y Li
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Pengyue Zhang
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA
| | - Shams Rashid
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Yang J Cheng
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Wenhui Li
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA
| | - William Schapiro
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Kathleen Gliganic
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Ann-Marie Yamashita
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Marie Grgas
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - Elizabeth Haag
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
| | - J Jane Cao
- St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, 101 Northern Blvd, Greenvale, NY 11548, USA.
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Kolbitsch C, Bastkowski R, Schäffter T, Prieto Vasquez C, Weiss K, Maintz D, Giese D. Respiratory motion corrected 4D flow using golden radial phase encoding. Magn Reson Med 2019; 83:635-644. [DOI: 10.1002/mrm.27918] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 06/26/2019] [Accepted: 07/04/2019] [Indexed: 01/14/2023]
Affiliation(s)
- Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- King's College London School of Biomedical Engineering and Imaging Sciences London United Kingdom
| | - Rene Bastkowski
- Department of Radiology University Hospital of Cologne Cologne Germany
| | - Tobias Schäffter
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- King's College London School of Biomedical Engineering and Imaging Sciences London United Kingdom
| | - Claudia Prieto Vasquez
- King's College London School of Biomedical Engineering and Imaging Sciences London United Kingdom
| | - Kilian Weiss
- Department of Radiology University Hospital of Cologne Cologne Germany
- Philips GmbH Healthcare Hamburg Germany
| | - David Maintz
- Department of Radiology University Hospital of Cologne Cologne Germany
| | - Daniel Giese
- Department of Radiology University Hospital of Cologne Cologne Germany
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Chen Z, Kang L, Xia L, Wang Q, Li Y, Hu X, Liu F, Huang F. Technical Note: Sequential combination of parallel imaging and dynamic artificial sparsity framework for rapid free-breathing golden-angle radial dynamic MRI: K-T ARTS-GROWL. Med Phys 2017; 45:202-213. [DOI: 10.1002/mp.12639] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 09/17/2017] [Accepted: 10/18/2017] [Indexed: 12/25/2022] Open
Affiliation(s)
- Zhifeng Chen
- Department of Biomedical Engineering; Zhejiang University; Hangzhou China
| | - Liyi Kang
- Department of Biomedical Engineering; Zhejiang University; Hangzhou China
| | - Ling Xia
- Department of Biomedical Engineering; Zhejiang University; Hangzhou China
- State Key Lab of CAD&CG; Zhejiang University; Hangzhou China
| | - Qiuliang Wang
- Division of Superconducting Magnet Science and Technology; Institute of Electrical Engineering; Chinese Academy of Sciences; Beijing China
| | - Yi Li
- Division of Superconducting Magnet Science and Technology; Institute of Electrical Engineering; Chinese Academy of Sciences; Beijing China
| | - Xinning Hu
- Division of Superconducting Magnet Science and Technology; Institute of Electrical Engineering; Chinese Academy of Sciences; Beijing China
| | - Feng Liu
- School of Information Technology and Electrical Engineering; The University of Queensland; Brisbane QLD Australia
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Hamilton J, Franson D, Seiberlich N. Recent advances in parallel imaging for MRI. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2017; 101:71-95. [PMID: 28844222 PMCID: PMC5927614 DOI: 10.1016/j.pnmrs.2017.04.002] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 04/09/2017] [Accepted: 04/17/2017] [Indexed: 05/22/2023]
Abstract
Magnetic Resonance Imaging (MRI) is an essential technology in modern medicine. However, one of its main drawbacks is the long scan time needed to localize the MR signal in space to generate an image. This review article summarizes some basic principles and recent developments in parallel imaging, a class of image reconstruction techniques for shortening scan time. First, the fundamentals of MRI data acquisition are covered, including the concepts of k-space, undersampling, and aliasing. It is demonstrated that scan time can be reduced by sampling a smaller number of phase encoding lines in k-space; however, without further processing, the resulting images will be degraded by aliasing artifacts. Nearly all modern clinical scanners acquire data from multiple independent receiver coil arrays. Parallel imaging methods exploit properties of these coil arrays to separate aliased pixels in the image domain or to estimate missing k-space data using knowledge of nearby acquired k-space points. Three parallel imaging methods-SENSE, GRAPPA, and SPIRiT-are described in detail, since they are employed clinically and form the foundation for more advanced methods. These techniques can be extended to non-Cartesian sampling patterns, where the collected k-space points do not fall on a rectangular grid. Non-Cartesian acquisitions have several beneficial properties, the most important being the appearance of incoherent aliasing artifacts. Recent advances in simultaneous multi-slice imaging are presented next, which use parallel imaging to disentangle images of several slices that have been acquired at once. Parallel imaging can also be employed to accelerate 3D MRI, in which a contiguous volume is scanned rather than sequential slices. Another class of phase-constrained parallel imaging methods takes advantage of both image magnitude and phase to achieve better reconstruction performance. Finally, some applications are presented of parallel imaging being used to accelerate MR Spectroscopic Imaging.
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Affiliation(s)
- Jesse Hamilton
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Dominique Franson
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Nicole Seiberlich
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
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Valvano G, Martini N, Huber A, Santelli C, Binter C, Chiappino D, Landini L, Kozerke S. Accelerating 4D flow MRI by exploiting low-rank matrix structure and hadamard sparsity. Magn Reson Med 2016; 78:1330-1341. [PMID: 27787911 DOI: 10.1002/mrm.26508] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 09/23/2016] [Accepted: 09/23/2016] [Indexed: 11/11/2022]
Affiliation(s)
- Giuseppe Valvano
- Department of Information Engineering; University of Pisa; Pisa Italy
- Fondazione G. Monasterio CNR-Regione Toscana; Massa Italy
| | - Nicola Martini
- Fondazione G. Monasterio CNR-Regione Toscana; Massa Italy
| | - Adrian Huber
- Institute for Biomedical Engineering; University and ETH Zurich; Zurich Switzerland
| | - Claudio Santelli
- Institute for Biomedical Engineering; University and ETH Zurich; Zurich Switzerland
| | - Christian Binter
- Institute for Biomedical Engineering; University and ETH Zurich; Zurich Switzerland
| | | | - Luigi Landini
- Department of Information Engineering; University of Pisa; Pisa Italy
- Fondazione G. Monasterio CNR-Regione Toscana; Massa Italy
| | - Sebastian Kozerke
- Institute for Biomedical Engineering; University and ETH Zurich; Zurich Switzerland
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Alexander MD, Yuan C, Rutman A, Tirschwell DL, Palagallo G, Gandhi D, Sekhar LN, Mossa-Basha M. High-resolution intracranial vessel wall imaging: imaging beyond the lumen. J Neurol Neurosurg Psychiatry 2016; 87:589-97. [PMID: 26746187 PMCID: PMC5504758 DOI: 10.1136/jnnp-2015-312020] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 11/23/2015] [Indexed: 01/21/2023]
Abstract
Accurate and timely diagnosis of intracranial vasculopathies is important due to significant risk of morbidity with delayed and/or incorrect diagnosis both from the disease process as well as inappropriate therapies. Conventional vascular imaging techniques for analysis of intracranial vascular disease provide limited information since they only identify changes to the vessel lumen. New advanced MR intracranial vessel wall imaging (IVW) techniques can allow direct characterisation of the vessel wall. These techniques can advance diagnostic accuracy and may potentially improve patient outcomes by better guided treatment decisions in comparison to previously available invasive and non-invasive techniques. While neuroradiological expertise is invaluable in accurate examination interpretation, clinician familiarity with the application and findings of the various vasculopathies on IVW can help guide diagnostic and therapeutic decision-making. This review article provides a brief overview of the technical aspects of IVW and discusses the IVW findings of various intracranial vasculopathies, differentiating characteristics and indications for when this technique can be beneficial in patient management.
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Affiliation(s)
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Aaron Rutman
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - David L Tirschwell
- Department of Neurology, University of Washington, Seattle, Washington, USA
| | - Gerald Palagallo
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Dheeraj Gandhi
- Department of Radiology, Neurology and Neurosurgery, University of Maryland, Baltimore, Maryland, USA
| | - Laligam N Sekhar
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Mahmud Mossa-Basha
- Department of Radiology, University of Washington, Seattle, Washington, USA
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Sun A, Zhao B, Ma K, Zhou Z, He L, Li R, Yuan C. Accelerated phase contrast flow imaging with direct complex difference reconstruction. Magn Reson Med 2016; 77:1036-1048. [PMID: 27016025 DOI: 10.1002/mrm.26184] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 01/31/2016] [Accepted: 02/05/2016] [Indexed: 11/09/2022]
Abstract
PURPOSE To propose and evaluate a new model-based reconstruction method for highly accelerated phase-contrast magnetic resonance imaging (PC-MRI) with sparse sampling. THEORY AND METHODS This work presents a new constrained reconstruction method based on low-rank and sparsity constraints to accelerate PC-MRI. More specifically, we formulate the image reconstruction problem into separate reconstructions of flow-reference image sequence and complex differences. We then utilize the joint partial separability and sparsity constraints to enable high quality reconstruction from highly undersampled (k,t)-space data. We further integrate the proposed method with ESPIRiT based parallel imaging model to effectively handle multichannel acquisition. RESULTS The proposed method was evaluated with in vivo data acquired from both 2D and 3D PC flow imaging experiments, and compared with several state-of-the-art methods. Experimental results demonstrate that the proposed method leads to more accurate velocity reconstruction from highly undersampled (k,t)-space data, and particularly superior capability of capturing the peak velocity of blood flow. In terms of flow visualization, blood flow patterns obtained from the proposed reconstruction also exhibit better agreement with those obtained from the fully sampled reference. CONCLUSION The proposed method achieves improved accuracy over several state-of-the-art methods for velocity reconstruction with highly accelerated (k,t)-space data. Magn Reson Med 77:1036-1048, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Aiqi Sun
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Bo Zhao
- Department of Electrical and Computer Engineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Ke Ma
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Zechen Zhou
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Le He
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Chun Yuan
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.,Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA
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Park S, Park J. Accelerated dynamic cardiac MRI exploiting sparse-Kalman-smoother self-calibration and reconstruction (k - t SPARKS). Phys Med Biol 2015; 60:3655-71. [PMID: 25884383 DOI: 10.1088/0031-9155/60/9/3655] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Accelerated dynamic MRI, which exploits spatiotemporal redundancies in k - t space and coil dimension, has been widely used to reduce the number of signal encoding and thus increase imaging efficiency with minimal loss of image quality. Nonetheless, particularly in cardiac MRI it still suffers from artifacts and amplified noise in the presence of time-drifting coil sensitivity due to relative motion between coil and subject (e.g. free breathing). Furthermore, a substantial number of additional calibrating signals is to be acquired to warrant accurate calibration of coil sensitivity. In this work, we propose a novel, accelerated dynamic cardiac MRI with sparse-Kalman-smoother self-calibration and reconstruction (k - t SPARKS), which is robust to time-varying coil sensitivity even with a small number of calibrating signals. The proposed k - t SPARKS incorporates Kalman-smoother self-calibration in k - t space and sparse signal recovery in x - f space into a single optimization problem, leading to iterative, joint estimation of time-varying convolution kernels and missing signals in k - t space. In the Kalman-smoother calibration, motion-induced uncertainties over the entire time frames were included in modeling state transition while a coil-dependent noise statistic in describing measurement process. The sparse signal recovery iteratively alternates with the self-calibration to tackle the ill-conditioning problem potentially resulting from insufficient calibrating signals. Simulations and experiments were performed using both the proposed and conventional methods for comparison, revealing that the proposed k - t SPARKS yields higher signal-to-error ratio and superior temporal fidelity in both breath-hold and free-breathing cardiac applications over all reduction factors.
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Affiliation(s)
- Suhyung Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon, 440-746, Republic of Korea
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