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Romanin L, Milani B, Roy CW, Yerly J, Bustin A, Si-mohamed S, Prsa M, Rutz T, Tenisch E, Schwitter J, Stuber M, Piccini D. Similarity-driven motion-resolved reconstruction for ferumoxytol-enhanced whole-heart MRI in congenital heart disease. PLoS One 2024; 19:e0304612. [PMID: 38870171 PMCID: PMC11175540 DOI: 10.1371/journal.pone.0304612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 05/15/2024] [Indexed: 06/15/2024] Open
Abstract
A similarity-driven multi-dimensional binning algorithm (SIMBA) reconstruction of free-running cardiac magnetic resonance imaging data was previously proposed. While very efficient and fast, the original SIMBA focused only on the reconstruction of a single motion-consistent cluster, discarding the remaining data acquired. However, the redundant data clustered by similarity may be exploited to further improve image quality. In this work, we propose a novel compressed sensing (CS) reconstruction that performs an effective regularization over the clustering dimension, thanks to the integration of inter-cluster motion compensation (XD-MC-SIMBA). This reconstruction was applied to free-running ferumoxytol-enhanced datasets from 24 patients with congenital heart disease, and compared to the original SIMBA, the same XD-MC-SIMBA reconstruction but without motion compensation (XD-SIMBA), and a 5D motion-resolved CS reconstruction using the free-running framework (FRF). The resulting images were compared in terms of lung-liver and blood-myocardium sharpness, blood-myocardium contrast ratio, and visible length and sharpness of the coronary arteries. Moreover, an automated image quality score (IQS) was assigned using a pretrained deep neural network. The lung-liver sharpness and blood-myocardium sharpness were significantly higher in XD-MC-SIMBA and FRF. Consistent with these findings, the IQS analysis revealed that image quality for XD-MC-SIMBA was improved in 18 of 24 cases, compared to SIMBA. We successfully tested the hypothesis that multiple motion-consistent SIMBA clusters can be exploited to improve the quality of ferumoxytol-enhanced cardiac MRI when inter-cluster motion-compensation is integrated as part of a CS reconstruction.
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Affiliation(s)
- Ludovica Romanin
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Bastien Milani
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Christopher W. Roy
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jérôme Yerly
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Aurélien Bustin
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux – INSERM U1045, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Pessac, France
| | - Salim Si-mohamed
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Villeurbanne, France
- Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, Bron, France
| | - Milan Prsa
- Division of Pediatric Cardiology, Woman-Mother-Child Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Rutz
- Division of Cardiology, Cardiovascular Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Estelle Tenisch
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Juerg Schwitter
- Division of Cardiology, Cardiovascular Department, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology&Medicine, University of Lausanne, UniL, Lausanne, Switzerland
- Cardiac MR Center of the University Hospital Lausanne, Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Davide Piccini
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
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Roy CW, Milani B, Yerly J, Si-Mohamed S, Romanin L, Bustin A, Tenisch E, Rutz T, Prsa M, Stuber M. Intra-bin correction and inter-bin compensation of respiratory motion in free-running five-dimensional whole-heart magnetic resonance imaging. J Cardiovasc Magn Reson 2024; 26:101037. [PMID: 38499269 PMCID: PMC10987330 DOI: 10.1016/j.jocmr.2024.101037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Free-running cardiac and respiratory motion-resolved whole-heart five-dimensional (5D) cardiovascular magnetic resonance (CMR) can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D CMR. METHODS Each component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N = 50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (intra-acquisition correction and inter-acquisition compensation of respiratory motion [IIMC] 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers. RESULTS Intra-bin motion correction provides significantly (p < 0.001) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 0.001) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 0.001) sharper IIMC 5D reconstructions (End-expiration (End-Exp): 0.45 ± 0.09, End-inspiration (End-Ins): 0.46 ± 0.10) relative to the previously established 5D implementation (End-Exp: 0.43 ± 0.08, End-Ins: 0.39 ± 0.09). Similarly, image scoring by three expert reviewers was significantly (p < 0.001) higher using IIMC 5D (End-Exp: 3.39 ± 0.44, End-Ins: 3.32 ± 0.45) relative to 5D images (End-Exp: 3.02 ± 0.54, End-Ins: 2.45 ± 0.52). CONCLUSION The proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scanning. Further investigation of the diagnostic impact of this framework and comparison to gold standards is needed to understand its full clinical utility, including exploration of respiratory-driven changes in physiological measurements of interest.
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Affiliation(s)
- Christopher W Roy
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Bastien Milani
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jérôme Yerly
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Salim Si-Mohamed
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, 7 Avenue Jean Capelle O, 69100 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500 Bron, France
| | - Ludovica Romanin
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Aurélien Bustin
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux - INSERM U1045, Avenue du Haut Lévêque, 33604 Pessac, France; Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604 Pessac, France
| | - Estelle Tenisch
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Rutz
- Service of Cardiology, Heart and Vessel Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Milan Prsa
- Division of Pediatric Cardiology, Woman-Mother-Child Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
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Zhou Z, Hu P, Qi H. Stop moving: MR motion correction as an opportunity for artificial intelligence. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-023-01144-5. [PMID: 38386151 DOI: 10.1007/s10334-023-01144-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/09/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.
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Affiliation(s)
- Zijian Zhou
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China.
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China.
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Hu P, Tong X, Lin L, Wang LV. Data-driven system matrix manipulation enabling fast functional imaging and intra-image nonrigid motion correction in tomography. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574504. [PMID: 38260429 PMCID: PMC10802502 DOI: 10.1101/2024.01.07.574504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Tomographic imaging modalities are described by large system matrices. Sparse sampling and tissue motion degrade system matrix and image quality. Various existing techniques improve the image quality without correcting the system matrices. Here, we compress the system matrices to improve computational efficiency (e.g., 42 times) using singular value decomposition and fast Fourier transform. Enabled by the efficiency, we propose (1) fast sparsely sampling functional imaging by incorporating a densely sampled prior image into the system matrix, which maintains the critical linearity while mitigating artifacts and (2) intra-image nonrigid motion correction by incorporating the motion as subdomain translations into the system matrix and reconstructing the translations together with the image iteratively. We demonstrate the methods in 3D photoacoustic computed tomography with significantly improved image qualities and clarify their applicability to X-ray CT and MRI or other types of imperfections due to the similarities in system matrices.
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Affiliation(s)
- Peng Hu
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xin Tong
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Li Lin
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Present address: College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Lihong V Wang
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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5
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Sheagren CD, Cao T, Patel JH, Chen Z, Lee HL, Wang N, Christodoulou AG, Wright GA. Motion-compensated T 1 mapping in cardiovascular magnetic resonance imaging: a technical review. Front Cardiovasc Med 2023; 10:1160183. [PMID: 37790594 PMCID: PMC10542904 DOI: 10.3389/fcvm.2023.1160183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.
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Affiliation(s)
- Calder D. Sheagren
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Jaykumar H. Patel
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Zihao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Hsu-Lei Lee
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Graham A. Wright
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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6
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Bissell MM, Raimondi F, Ait Ali L, Allen BD, Barker AJ, Bolger A, Burris N, Carhäll CJ, Collins JD, Ebbers T, Francois CJ, Frydrychowicz A, Garg P, Geiger J, Ha H, Hennemuth A, Hope MD, Hsiao A, Johnson K, Kozerke S, Ma LE, Markl M, Martins D, Messina M, Oechtering TH, van Ooij P, Rigsby C, Rodriguez-Palomares J, Roest AAW, Roldán-Alzate A, Schnell S, Sotelo J, Stuber M, Syed AB, Töger J, van der Geest R, Westenberg J, Zhong L, Zhong Y, Wieben O, Dyverfeldt P. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. J Cardiovasc Magn Reson 2023; 25:40. [PMID: 37474977 PMCID: PMC10357639 DOI: 10.1186/s12968-023-00942-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/30/2023] [Indexed: 07/22/2023] Open
Abstract
Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.
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Affiliation(s)
- Malenka M Bissell
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), LIGHT Laboratories, Clarendon Way, University of Leeds, Leeds, LS2 9NL, UK.
| | | | - Lamia Ait Ali
- Institute of Clinical Physiology CNR, Massa, Italy
- Foundation CNR Tuscany Region G. Monasterio, Massa, Italy
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alex J Barker
- Department of Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Center, Aurora, USA
| | - Ann Bolger
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Nicholas Burris
- Department of Radiology, University of Michigan, Ann Arbor, USA
| | - Carl-Johan Carhäll
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Tino Ebbers
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Alex Frydrychowicz
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Julia Geiger
- Department of Diagnostic Imaging, University Children's Hospital, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Hojin Ha
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, South Korea
| | - Anja Hennemuth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael D Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Albert Hsiao
- Department of Radiology, University of California, San Diego, CA, USA
| | - Kevin Johnson
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Liliana E Ma
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Duarte Martins
- Department of Pediatric Cardiology, Hospital de Santa Cruz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Marci Messina
- Department of Radiology, Northwestern Medicine, Chicago, IL, USA
| | - Thekla H Oechtering
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Pim van Ooij
- Department of Radiology & Nuclear Medicine, Amsterdam Cardiovascular Sciences, Amsterdam Movement Sciences, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Department of Pediatric Cardiology, Division of Pediatrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cynthia Rigsby
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Imaging, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jose Rodriguez-Palomares
- Department of Cardiology, Hospital Universitari Vall d´Hebron,Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red-CV, CIBER CV, Madrid, Spain
| | - Arno A W Roest
- Department of Pediatric Cardiology, Willem-Alexander's Children Hospital, Leiden University Medical Center and Center for Congenital Heart Defects Amsterdam-Leiden, Leiden, The Netherlands
| | | | - Susanne Schnell
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Physics, Institute of Physics, University of Greifswald, Greifswald, Germany
| | - Julio Sotelo
- School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile
| | - Matthias Stuber
- Département de Radiologie Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Ali B Syed
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Johannes Töger
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos Westenberg
- CardioVascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Liang Zhong
- National Heart Centre Singapore, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Yumin Zhong
- Department of Radiology, School of Medicine, Shanghai Children's Medical Center Affiliated With Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Oliver Wieben
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Petter Dyverfeldt
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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7
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Falcão MBL, Rossi GMC, Rutz T, Prša M, Tenisch E, Ma L, Weiss EK, Baraboo JJ, Yerly J, Markl M, Stuber M, Roy CW. Focused navigation for respiratory-motion-corrected free-running radial 4D flow MRI. Magn Reson Med 2023; 90:117-132. [PMID: 36877140 PMCID: PMC10149606 DOI: 10.1002/mrm.29634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/07/2023]
Abstract
PURPOSE To validate a respiratory motion correction method called focused navigation (fNAV) for free-running radial whole-heart 4D flow MRI. METHODS Using fNAV, respiratory signals derived from radial readouts are converted into three orthogonal displacements, which are then used to correct respiratory motion in 4D flow datasets. Hundred 4D flow acquisitions were simulated with non-rigid respiratory motion and used for validation. The difference between generated and fNAV displacement coefficients was calculated. Vessel area and flow measurements from 4D flow reconstructions with (fNAV) and without (uncorrected) motion correction were compared to the motion-free ground-truth. In 25 patients, the same measurements were compared between fNAV 4D flow, 2D flow, navigator-gated Cartesian 4D flow, and uncorrected 4D flow datasets. RESULTS For simulated data, the average difference between generated and fNAV displacement coefficients was 0.04± $$ \pm $$ 0.32 mm and 0.31± $$ \pm $$ 0.35 mm in the x and y directions, respectively. In the z direction, this difference was region-dependent (0.02± $$ \pm $$ 0.51 mm up to 5.85± $$ \pm $$ 3.41 mm). For all measurements (vessel area, net volume, and peak flow), the average difference from ground truth was higher for uncorrected 4D flow datasets (0.32± $$ \pm $$ 0.11 cm2 , 11.1± $$ \pm $$ 3.5 mL, and 22.3± $$ \pm $$ 6.0 mL/s) than for fNAV 4D flow datasets (0.10± $$ \pm $$ 0.03 cm2 , 2.6± $$ \pm $$ 0.7 mL, and 5.1± 0 $$ \pm 0 $$ .9 mL/s, p < 0.05). In vivo, average vessel area measurements were 4.92± $$ \pm $$ 2.95 cm2 , 5.06± $$ \pm $$ 2.64 cm2 , 4.87± $$ \pm $$ 2.57 cm2 , 4.87± $$ \pm $$ 2.69 cm2 , for 2D flow and fNAV, navigator-gated and uncorrected 4D flow datasets, respectively. In the ascending aorta, all 4D flow datasets except for the fNAV reconstruction had significantly different vessel area measurements from 2D flow. Overall, 2D flow datasets demonstrated the strongest correlation to fNAV 4D flow for both net volume (r2 = 0.92) and peak flow (r2 = 0.94), followed by navigator-gated 4D flow (r2 = 0.83 and r2 = 0.86, respectively), and uncorrected 4D flow (r2 = 0.69 and r2 = 0.86, respectively). CONCLUSION fNAV corrected respiratory motion in vitro and in vivo, resulting in fNAV 4D flow measurements that are comparable to those derived from 2D flow and navigator-gated Cartesian 4D flow datasets, with improvements over those from uncorrected 4D flow.
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Affiliation(s)
- Mariana B. L. Falcão
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Giulia M. C. Rossi
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Tobias Rutz
- Service of Cardiology, Centre de Resonance Magnétique Cardiaque (CRMC), Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Milan Prša
- Woman-Mother-Child Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Estelle Tenisch
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Liliana Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois USA
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois USA
| | - Elizabeth K. Weiss
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois USA
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois USA
| | - Justin J. Baraboo
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois USA
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois USA
| | - Jérôme Yerly
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Michael Markl
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois USA
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois USA
| | - Matthias Stuber
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Christopher W. Roy
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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8
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Krishnamoorthy G, Tourais J, Smink J, Breeuwer M, Kouwenhoven M. Free-breathing 2D radial cine MRI with respiratory auto-calibrated motion correction (RAMCO). Magn Reson Med 2023; 89:977-989. [PMID: 36346081 PMCID: PMC10100319 DOI: 10.1002/mrm.29499] [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: 02/09/2022] [Revised: 09/12/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To develop a free-breathing (FB) 2D radial balanced steady-state free precession cine cardiac MRI method with 100% respiratory gating efficiency using respiratory auto-calibrated motion correction (RAMCO) based on a motion-sensing camera. METHODS The signal from a respiratory motion-sensing camera was recorded during a FB retrospectively electrocardiogram triggered 2D radial balanced steady-state free precession acquisition using pseudo-tiny-golden-angle ordering. With RAMCO, for each acquisition the respiratory signal was retrospectively auto-calibrated by applying different linear translations, using the resulting in-plane image sharpness as a criterium. The auto-calibration determines the optimal magnitude of the linear translations for each of the in-plane directions to minimize motion blurring caused by bulk respiratory motion. Additionally, motion-weighted density compensation was applied during radial gridding to minimize through-plane and non-bulk motion blurring. Left ventricular functional parameters and sharpness scores of FB radial cine were compared with and without RAMCO, and additionally with conventional breath-hold Cartesian cine on 9 volunteers. RESULTS FB radial cine with RAMCO had similar sharpness scores as conventional breath-hold Cartesian cine and the left ventricular functional parameters agreed. For FB radial cine, RAMCO reduced respiratory motion artifacts with a statistically significant difference in sharpness scores (P < 0.05) compared to reconstructions without motion correction. CONCLUSION 2D radial cine imaging with RAMCO allows evaluation of left ventricular functional parameters in FB with 100% respiratory efficiency. It eliminates the need for breath-holds, which is especially valuable for patients with no or impaired breath-holding capacity. Validation of the proposed method on patients is warranted.
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Affiliation(s)
- Guruprasad Krishnamoorthy
- Department MR R&D-Clinical Science, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Joao Tourais
- Department MR R&D-Clinical Science, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jouke Smink
- Department MR R&D-Clinical Science, Best, The Netherlands
| | - Marcel Breeuwer
- Department MR R&D-Clinical Science, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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9
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Ariyurek C, Wallace TE, Kober T, Kurugol S, Afacan O. Prospective motion correction in kidney MRI using FID navigators. Magn Reson Med 2023; 89:276-285. [PMID: 36063497 PMCID: PMC9670860 DOI: 10.1002/mrm.29424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/30/2022] [Accepted: 08/05/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time. METHODS FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique. RESULTS Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method. CONCLUSION The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice.
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Affiliation(s)
- Cemre Ariyurek
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sila Kurugol
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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10
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Lopez Kolkovsky AL, Carlier PG, Marty B, Meyerspeer M. Interleaved and simultaneous multi-nuclear magnetic resonance in vivo. Review of principles, applications and potential. NMR IN BIOMEDICINE 2022; 35:e4735. [PMID: 35352440 PMCID: PMC9542607 DOI: 10.1002/nbm.4735] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/03/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
Magnetic resonance signals from different nuclei can be excited or received at the same time,rendering simultaneous or rapidly interleaved multi-nuclear acquisitions feasible. The advan-tages are a reduction of total scan time compared to sequential multi-nuclear acquisitions or that additional information from heteronuclear data is obtained at thesame time and anatomical position. Information content can be qualitatively increased by delivering a more comprehensive MR-based picture of a transient state (such as an exercise bout). Also, combiningnon-proton MR acquisitions with 1 Hinformation (e.g., dynamic shim updates and motion correction) can be used to improve data quality during long scans and benefits image coregistration. This work reviews the literature on interleaved and simultaneous multi-nuclear MRI and MRS in vivo. Prominent use cases for this methodology in clinical and research applications are brain and muscle, but studies have also been carried out in other targets, including the lung, knee, breast and heart. Simultaneous multi-nuclear measurements in the liver and kidney have also been performed, but exclusively in rodents. In this review, a consistent nomenclature is proposed, to help clarify the terminology used for this principle throughout the literature on in-vivo MR. An overview covers the basic principles, the technical requirements on the MR scanner and the implementations realised either by MR system vendors or research groups, from the early days until today. Considerations regarding the multi-tuned RF coils required and heteronuclear polarisation interactions are briefly discussed, and fields for future in-vivo applications for interleaved multi-nuclear MR pulse sequences are identified.
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Affiliation(s)
- Alfredo L. Lopez Kolkovsky
- NMR Laboratory, Neuromuscular Investigation CenterInstitute of MyologyParisFrance
- NMR laboratoryCEA, DRF, IBFJParisFrance
| | - Pierre G. Carlier
- NMR Laboratory, Neuromuscular Investigation CenterInstitute of MyologyParisFrance
- NMR laboratoryCEA, DRF, IBFJParisFrance
| | - Benjamin Marty
- NMR Laboratory, Neuromuscular Investigation CenterInstitute of MyologyParisFrance
- NMR laboratoryCEA, DRF, IBFJParisFrance
| | - Martin Meyerspeer
- High‐Field MR Center, Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
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11
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Qi H, Lv Z, Hu J, Xu J, Botnar R, Prieto C, Hu P. Accelerated 3D free-breathing high-resolution myocardial T 1ρ mapping at 3 Tesla. Magn Reson Med 2022; 88:2520-2531. [PMID: 36054715 DOI: 10.1002/mrm.29417] [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: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/27/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a fast free-breathing whole-heart high-resolution myocardial T1ρ mapping technique with robust spin-lock preparation that can be performed at 3 Tesla. METHODS An adiabatically excited continuous-wave spin-lock module, insensitive to field inhomogeneities, was implemented with an electrocardiogram-triggered low-flip angle spoiled gradient echo sequence with variable-density 3D Cartesian undersampling at a 3 Tesla whole-body scanner. A saturation pulse was performed at the beginning of each cardiac cycle to null the magnetization before T1ρ preparation. Multiple T1ρ -weighted images were acquired with T1ρ preparations with different spin-lock times in an interleaved fashion. Respiratory self-gating approach was adopted along with localized autofocus to enable 3D translational motion correction of the data acquired in each heartbeat. After motion correction, multi-contrast locally low-rank reconstruction was performed to reduce undersampling artifacts. The accuracy and feasibility of the 3D T1ρ mapping technique was investigated in phantoms and in vivo in 10 healthy subjects compared with the 2D T1ρ mapping. RESULTS The 3D T1ρ mapping technique provided similar phantom T1ρ measurements in the range of 25-120 ms to the 2D T1ρ mapping reference over a wide range of simulated heart rates. With the robust adiabatically excited continuous-wave spin-lock preparation, good quality 2D and 3D in vivo T1ρ -weighted images and T1ρ maps were obtained. Myocardial T1ρ values with the 3D T1ρ mapping were slightly longer than 2D breath-hold measurements (septal T1ρ : 52.7 ± 1.4 ms vs. 50.2 ± 1.8 ms, P < 0.01). CONCLUSION A fast 3D free-breathing whole-heart T1ρ mapping technique was proposed for T1ρ quantification at 3 T with isotropic spatial resolution (2 mm3 ) and short scan time of ∼4.5 min.
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Affiliation(s)
- Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, People's Republic of China
| | - Zhenfeng Lv
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, People's Republic of China
| | - Junpu Hu
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Jian Xu
- UIH America, Inc., Houston, Texas
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, People's Republic of China
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12
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Ljungberg E, Wood TC, Solana AB, Williams SCR, Barker GJ, Wiesinger F. Motion corrected silent ZTE neuroimaging. Magn Reson Med 2022; 88:195-210. [PMID: 35381110 PMCID: PMC9321117 DOI: 10.1002/mrm.29201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/16/2022] [Accepted: 01/28/2022] [Indexed: 11/11/2022]
Abstract
Purpose To develop self‐navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion. Methods The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self‐navigation by using interleaved 3D phyllotaxis k‐space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co‐registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k‐space trajectory and raw data and reconstructed into a final, high‐resolution ZTE image. Results MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self‐navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA). Conclusion Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.
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Affiliation(s)
- Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Tobias C Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Florian Wiesinger
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,GE Healthcare, Munich, Germany
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13
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Continuous cardiac thermometry via simultaneous catheter tracking and undersampled radial golden angle acquisition for radiofrequency ablation monitoring. Sci Rep 2022; 12:4006. [PMID: 35256627 PMCID: PMC8901729 DOI: 10.1038/s41598-022-06927-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 01/24/2022] [Indexed: 01/18/2023] Open
Abstract
The complexity of the MRI protocol is one of the factors limiting the clinical adoption of MR temperature mapping for real-time monitoring of cardiac ablation procedures and a push-button solution would ease its use. Continuous gradient echo golden angle radial acquisition combined with intra-scan motion correction and undersampled temperature determination could be a robust and more user-friendly alternative than the ultrafast GRE-EPI sequence which suffers from sensitivity to magnetic field susceptibility artifacts and requires ECG-gating. The goal of this proof-of-concept work is to establish the temperature uncertainty as well as the spatial and temporal resolutions achievable in an Agar-gel phantom and in vivo using this method. GRE radial golden angle acquisitions were used to monitor RF ablations in a phantom and in vivo in two sheep hearts with different slice orientations. In each case, 2D rigid motion correction based on catheter micro-coil signal, tracking its motion, was performed and its impact on the temperature imaging was assessed. The temperature uncertainty was determined for three spatial resolutions (1 × 1 × 3 mm3, 2 × 2 × 3 mm3, and 3 × 3 × 3 mm3) and three temporal resolutions (0.48, 0.72, and 0.97 s) with undersampling acceleration factors ranging from 2 to 17. The combination of radial golden angle GRE acquisition, simultaneous catheter tracking, intra-scan 2D motion correction, and undersampled thermometry enabled temperature monitoring in the myocardium in vivo during RF ablations with high temporal (< 1 s) and high spatial resolution. The temperature uncertainty ranged from 0.2 ± 0.1 to 1.8 ± 0.2 °C for the various temporal and spatial resolutions and, on average, remained superior to the uncertainty of an EPI acquisition while still allowing clinical monitoring of the RF ablation process. The proposed method is a robust and promising alternative to EPI acquisition to monitor in vivo RF cardiac ablations. Further studies remain required to improve the temperature uncertainty and establish its clinical applicability.
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14
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Schneider A, Cruz G, Munoz C, Hajhosseiny R, Kuestner T, Kunze KP, Neji R, Botnar RM, Prieto C. Whole-heart non-rigid motion corrected coronary MRA with autofocus virtual 3D iNAV. Magn Reson Imaging 2022; 87:169-176. [PMID: 34999163 DOI: 10.1016/j.mri.2022.01.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/04/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction. METHODS Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC. RESULTS Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant. CONCLUSION The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent.
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Affiliation(s)
- Alina Schneider
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Medical Image and Data Analysis, Department of Interventional and Diagnostic Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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15
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Kustner T, Pan J, Qi H, Cruz G, Gilliam C, Blu T, Yang B, Gatidis S, Botnar R, Prieto C. LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3686-3697. [PMID: 34242163 DOI: 10.1109/tmi.2021.3096131] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.
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16
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Raimondi F, Martins D, Coenen R, Panaioli E, Khraiche D, Boddaert N, Bonnet D, Atkins M, El-Said H, Alshawabkeh L, Hsiao A. Prevalence of Venovenous Shunting and High-Output State Quantified with 4D Flow MRI in Patients with Fontan Circulation. Radiol Cardiothorac Imaging 2021; 3:e210161. [PMID: 34934948 PMCID: PMC8686005 DOI: 10.1148/ryct.210161] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/04/2021] [Accepted: 11/12/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To assess the ability of four-dimensional (4D) flow MRI to quantify flow volume of the Fontan circuit, including the frequency and hemodynamic contribution of systemic-to-pulmonary venovenous collateral vessels. MATERIALS AND METHODS In this retrospective study, patients with Fontan circulation were included from three institutions (2017-2021). Flow measurements were performed at several locations along the circuit by two readers, and collateral shunt volumes were quantified. The frequency of venovenous collaterals and structural defects were tabulated from concurrent MR angiography, contemporaneous CT, or catheter angiography and related to Fontan clinical status. Statistical analysis included Pearson and Spearman correlation and Bland-Altman analysis. RESULTS Seventy-five patients (mean age, 20 years; range, 5-58 years; 46 female and 29 male patients) were included. Interobserver agreement was high for aortic output, pulmonary arteries, pulmonary veins, superior vena cava (Glenn shunt), and inferior vena cava (Fontan conduit) (range, ρ = 0.913-0.975). Calculated shunt volume also showed strong agreement, on the basis of the difference between aortic and pulmonary flow (ρ = 0.935). A total of 37 of 75 (49%) of the patients exhibited shunts exceeding 1.00 L/min, 81% (30 of 37) of whom had pulmonary venous or atrial flow volume step-ups and corresponding venovenous collaterals. A total of 12% of patients (nine of 75) exhibited a high-output state (>4 L/min/m2), most of whom had venovenous shunts exceeding 30% of cardiac output. CONCLUSION Fontan flow and venovenous shunting can be reliably quantified at 4D flow MRI; high-output states were found in a higher proportion of patients than expected, among whom venovenous collaterals were common and constituted a substantial proportion of cardiac output.Keywords: Pediatrics, MR Angiography, Cardiac, Technology Assessment, Hemodynamics/Flow Dynamics, Congenital Supplemental material is available for this article. © RSNA, 2021.
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Affiliation(s)
- Francesca Raimondi
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Duarte Martins
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Raluca Coenen
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Elena Panaioli
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Diala Khraiche
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Nathalie Boddaert
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Damien Bonnet
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Melany Atkins
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Howaida El-Said
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Laith Alshawabkeh
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
| | - Albert Hsiao
- From the Unité Médico-Chirurgicale de Cardiologie
Congénitale et Pédiatrique, Centre de Référence des
Maladies Cardiaques Congénitales Complexes-M3C, Hôpital
Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
(F.R., D.K., D.B.); Pediatric Radiology Unit, Hôpital Universitaire
Necker-Enfants Malades, Université de Paris, Paris, France (F.R., E.P.,
N.B.); Decision and Bayesian Computation, Computation Biology Department, CNRS,
URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris,
France (F.R.); School of Biomedical Engineering & Imaging Sciences,
King’s College London, Lambeth Wing, St Thomas’ Hospital, London,
England (F.R.); Department of Pediatric Cardiology, Hospital de Santa Cruz,
Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal (D.M.); Radiology and
Cardiology Unit, Erasmus MC, Rotterdam, the Netherlands (R.C.); Fairfax
Radiological Consultants, Fairfax, Va (M.A.); and Departments of Pediatric
Cardiology (H.E.S.), Cardiovascular Medicine (L.A.), and Radiology (A.H.),
University of California, San Diego, 9300 Campus Point Dr, Room 7756, La Jolla,
CA 92037-7756
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17
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You S, Masutani EM, Alley MT, Vasanawala SS, Taub PR, Liau J, Roberts AC, Hsiao A. Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI. Radiology 2021; 302:584-592. [PMID: 34846200 PMCID: PMC8893183 DOI: 10.1148/radiol.2021211270] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to correct. Purpose To assess the feasibility of using deep learning to automatically perform image-based background phase error correction in 4D flow MRI and to compare its effectiveness relative to manual image-based correction. Materials and Methods A convenience sample of 139 abdominopelvic 4D flow MRI acquisitions performed between January 2016 and July 2020 was retrospectively collected. Manual phase error correction was performed using dedicated imaging software and served as the reference standard. After reserving 40 examinations for testing, the remaining examinations were randomly divided into training (86% [85 of 99]) and validation (14% [14 of 99]) data sets to train a multichannel three-dimensional U-Net convolutional neural network. Flow measurements were obtained for the infrarenal aorta, common iliac arteries, common iliac veins, and inferior vena cava. Statistical analyses included Pearson correlation, Bland-Altman analysis, and F tests with Bonferroni correction. Results A total of 139 patients (mean age, 47 years ± 14 [standard deviation]; 108 women) were included. Inflow-outflow correlation improved after manual correction (ρ = 0.94, P < .001) compared with that before correction (ρ = 0.50, P < .001). Automated correction showed similar results (ρ = 0.91, P < .001) and demonstrated very strong correlation with manual correction (ρ = 0.98, P < .001). Both correction methods reduced inflow-outflow variance, improving mean difference from -0.14 L/min (95% limits of agreement: -1.61, 1.32) (uncorrected) to 0.05 L/min (95% limits of agreement: -0.32, 0.42) (manually corrected) and 0.05 L/min (95% limits of agreement: -0.38, 0.49) (automatically corrected). There was no significant difference in inflow-outflow variance between manual and automated correction methods (P = .10). Conclusion Deep learning automated phase error correction reduced inflow-outflow bias and variance of volumetric flow measurements in four-dimensional flow MRI, achieving results comparable with manual image-based phase error correction. © RSNA, 2021 See also the editorial by Roldán-Alzate and Grist in this issue.
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Affiliation(s)
- Sophie You
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Evan M. Masutani
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Marcus T. Alley
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Shreyas S. Vasanawala
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Pam R. Taub
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Joy Liau
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Anne C. Roberts
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
| | - Albert Hsiao
- From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.)
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18
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Cruz G, Qi H, Jaubert O, Kuestner T, Schneider T, Botnar RM, Prieto C. Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting. Magn Reson Med 2021; 87:746-763. [PMID: 34601737 DOI: 10.1002/mrm.29027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 08/12/2021] [Accepted: 09/09/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF). METHODS Generalized motion-corrected (MC) reconstructions have been developed for steady-state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low-rank dictionary-based compression into the generalized MC model to reconstruct MC singular images, reducing motion artifacts in the resulting parametric maps. The proposed LRMC reconstruction was applied for cardiac motion correction in 2D myocardial MRF (T1 and T2 ) with extended cardiac acquisition window (~450 ms) and for respiratory MC in free-breathing 3D myocardial and 3D liver MRF. Experiments were performed in phantom and 22 healthy subjects. The proposed approach was compared with reference spin echo (phantom) and with 2D electrocardiogram-triggered/breath-hold MOLLI and T2 gradient-and-spin echo conventional maps (in vivo 2D and 3D myocardial MRF). RESULTS Phantom results were in general agreement with reference spin-echo measurements, presenting relative errors of approximately 5.4% and 5.5% for T1 and short T2 (<100 ms), respectively. The proposed LRMC MRF reduced residual blurring artifacts with respect to no MC for cardiac or respiratory motion in all cases (2D and 3D myocardial, 3D abdominal). In 2D myocardial MRF, left-ventricle T1 values were 1150 ± 41 ms for LRMC MRF and 1010 ± 56 ms for MOLLI; T2 values were 43.8 ± 2.3 ms for LRMC MRF and 49.5 ± 4.5 ms for T2 gradient and spin echo. Corresponding measurements for 3D myocardial MRF were 1085 ± 30 ms and 1062 ± 29 ms for T1 , and 43.5 ± 1.9 ms and 51.7 ± 1.7 ms for T2 . For 3D liver, LRMC MRF measured liver T1 at 565 ± 44 ms and liver T2 at 35.4 ± 2.4 ms. CONCLUSION The proposed LRMC reconstruction enabled generalized (nonrigid) MC for 2D and 3D MRF, both for cardiac and respiratory motion. The proposed approach reduced motion artifacts in the MRF maps with respect to no motion compensation and achieved good agreement with reference measurements.
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Affiliation(s)
- Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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19
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Kim Y, Hwang J, Hong SS, Kim HJ, Chang YW, Sung J, Nickel D. Clinical Feasibility of High-Resolution Contrast-Enhanced Dynamic T1-Weighted Magnetic Resonance Imaging of the Upper Abdomen Using Compressed Sensing. J Comput Assist Tomogr 2021; 45:669-677. [PMID: 34546676 DOI: 10.1097/rct.0000000000001221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The objective of this study was to evaluate the clinical feasibility of high-resolution contrast-enhanced dynamic T1-weighted imaging (T1WI) using compressed sensing (CS) in magnetic resonance imaging. METHODS This study retrospectively included 35 patients who underwent dynamic T1WI using volumetric interpolated breath-hold examination (VIBE) with CS reconstruction (CS-VIBE) and 35 patients with conventional VIBE for comparison. Two observers assessed the liver and pancreas edges, hepatic artery, motion artifacts, and overall image quality. Quantitative analysis was performed by measuring signal intensity and image noise. RESULTS The results showed that CS-VIBE achieved significantly better anatomic delineation of the liver and pancreas edges and hepatic artery clarity than VIBE (P < 0.001). There were no significant differences in motion artifacts in dynamic phases and overall image quality. The signal intensities and INs of CS-VIBE were higher than VIBE. CONCLUSIONS High-resolution dynamic T1WI using CS provides better anatomic delineation with comparable or better overall image quality than conventional VIBE.
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Affiliation(s)
- Yeonsoo Kim
- From the Department of Radiology, Soonchunhyang University College of Medicine, Seoul Hospital
| | - Jiyoung Hwang
- From the Department of Radiology, Soonchunhyang University College of Medicine, Seoul Hospital
| | - Seong Sook Hong
- From the Department of Radiology, Soonchunhyang University College of Medicine, Seoul Hospital
| | - Hyun-Joo Kim
- From the Department of Radiology, Soonchunhyang University College of Medicine, Seoul Hospital
| | - Yun-Woo Chang
- From the Department of Radiology, Soonchunhyang University College of Medicine, Seoul Hospital
| | - JaeKon Sung
- Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Dominik Nickel
- Application Development, Siemens Healthcare GmbH, Erlangen, Germany
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20
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Brunsing RL, Brown D, Almahoud H, Kono Y, Loomba R, Vodkin I, Sirlin CB, Alley MT, Vasanawala SS, Hsiao A. Quantification of the Hemodynamic Changes of Cirrhosis with Free-Breathing Self-Navigated MRI. J Magn Reson Imaging 2021; 53:1410-1421. [PMID: 33594733 PMCID: PMC9161739 DOI: 10.1002/jmri.27488] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/08/2020] [Accepted: 12/10/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Non-invasive assessment of the hemodynamic changes of cirrhosis might help guide management of patients with liver disease but are currently limited. PURPOSE To determine whether free-breathing 4D flow MRI can be used to quantify the hemodynamic effects of cirrhosis and introduce hydraulic circuit indexes of severity. STUDY TYPE Retrospective. POPULATION Forty-seven patients including 26 with cirrhosis. FIELD STRENGTH/SEQUENCE 3 T/free-breathing 4D flow MRI with soft gating and golden-angle view ordering. ASSESSMENT Measurements of the supra-celiac abdominal aorta, supra-renal abdominal aorta (SRA), celiac trunk (CeT), superior mesenteric artery (SMA), splenic artery (SpA), common hepatic artery (CHA), portal vein (PV), and supra-renal inferior vena cava (IVC) were made by two radiologists. Measures of hepatic vascular resistance (hepatic arterial relative resistance [HARR]; portal resistive index [PRI]) were proposed and calculated. STATISTICAL ANALYSIS Bland-Altman, Pearson's correlation, Tukey's multiple comparison, and Cohen's kappa. P < 0.05 was considered significant. RESULTS Forty-four of 47 studies yielded adequate image quality for flow quantification (94%). Arterial structures showed high inter-reader concordance (range; ρ = 0.948-0.987) and the IVC (ρ = 0.972), with moderate concordance in the PV (ρ = 0.866). Conservation of mass analysis showed concordance between large vessels (SRA vs. IVC; ρ = 0.806), small vessels (celiac vs. CHA + SpA; ρ = 0.939), and across capillary beds (CeT + SMA vs. PV; ρ = 0.862). Splanchnic flow was increased in patients with portosystemic shunting (PSS) relative to control patients and patients with cirrhosis without PSS (P < 0.05, difference range 0.11-0.68 liter/m). HARR was elevated and PRI was decreased in patients with PSS (3.55 and 1.49, respectively) compared to both the control (2.11/3.18) and non-PSS (2.11/2.35) cohorts. DATA CONCLUSION 4D flow MRI with self-navigation was technically feasible, showing promise in quantifying the hemodynamic effects of cirrhosis. Proposed quantitative metrics of hepatic vascular resistance correlated with PSS. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Ryan L Brunsing
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Dustin Brown
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Hashem Almahoud
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Yuko Kono
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Rohit Loomba
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, California, USA
- Division of Epidemiology, Department of Family Medicine and Preventive Medicine, University of California San Diego, La Jolla, California, USA
- NAFLD Research Center, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Irene Vodkin
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Claude B Sirlin
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Marcus T Alley
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | | | - Albert Hsiao
- Department of Radiology, University of California San Diego, La Jolla, California, USA
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21
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Roy CW, Heerfordt J, Piccini D, Rossi G, Pavon AG, Schwitter J, Stuber M. Motion compensated whole-heart coronary cardiovascular magnetic resonance angiography using focused navigation (fNAV). J Cardiovasc Magn Reson 2021; 23:33. [PMID: 33775246 PMCID: PMC8006382 DOI: 10.1186/s12968-021-00717-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 01/28/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Radial self-navigated (RSN) whole-heart coronary cardiovascular magnetic resonance angiography (CCMRA) is a free-breathing technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CCMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). METHODS We applied fNAV to 500 data sets from a numerical simulation, 22 healthy subjects, and 549 cardiac patients. In each of these cohorts we compared fNAV to RSN and respiratory resolved extradimensional golden-angle radial sparse parallel (XD-GRASP) reconstructions of the same data. Reconstruction times for each method were recorded. Motion estimate accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Percent vessel sharpness was measured in all simulated data sets and healthy subjects, and a subset of patients. Finally, subjective image quality analysis was performed by a blinded expert reviewer who chose the best image for each in vivo data set and scored on a Likert scale 0-4 in a subset of patients by two reviewers in consensus. RESULTS The reconstruction time for fNAV images was significantly higher than RSN (6.1 ± 2.1 min vs 1.4 ± 0.3, min, p < 0.025) but significantly lower than XD-GRASP (25.6 ± 7.1, min, p < 0.025). Overall, there is high correlation between the fNAV and reference displacement estimates across all data sets (0.73 ± 0.29). For simulated data, healthy subjects, and patients, fNAV lead to significantly sharper coronary arteries than all other reconstruction methods (p < 0.01). Finally, in a blinded evaluation by an expert reviewer fNAV was chosen as the best image in 444 out of 571 data sets (78%; p < 0.001) and consensus grades of fNAV images (2.6 ± 0.6) were significantly higher (p < 0.05) than uncorrected (1.7 ± 0.7), RSN (1.9 ± 0.6), and XD-GRASP (1.8 ± 0.8). CONCLUSION fNAV is a promising technique for improving the quality of RSN free-breathing 3D whole-heart CCMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions. Further study of the diagnostic impact of this technique is therefore warranted to evaluate its full clinical utility.
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Affiliation(s)
- Christopher W Roy
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue de Bugnon 46, BH-7-84, 1011, Lausanne, Switzerland.
| | - John Heerfordt
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue de Bugnon 46, BH-7-84, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology (ACIT), Siemens Healthcare AG, Lausanne, Switzerland
| | - Davide Piccini
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue de Bugnon 46, BH-7-84, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology (ACIT), Siemens Healthcare AG, Lausanne, Switzerland
| | - Giulia Rossi
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue de Bugnon 46, BH-7-84, 1011, Lausanne, Switzerland
| | - Anna Giulia Pavon
- Division of Cardiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Juerg Schwitter
- Division of Cardiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Director CMR-Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue de Bugnon 46, BH-7-84, 1011, Lausanne, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
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22
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Ghodrati V, Bydder M, Ali F, Gao C, Prosper A, Nguyen KL, Hu P. Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning. NMR IN BIOMEDICINE 2021; 34:e4433. [PMID: 33258197 PMCID: PMC10193526 DOI: 10.1002/nbm.4433] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 09/18/2020] [Accepted: 10/02/2020] [Indexed: 05/20/2023]
Abstract
The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U-net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free-breathing motion-corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion-correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion-corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion-corrupted images and motion-corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion-corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion-corrupted images, motion-corrected images and breath-held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric-paired comparisons showed that there was significant difference between the image quality scores of the motion-corrupted and breath-held images (P < .05); however, after correction there was no significant difference between the image quality scores of the motion-corrected and breath-held images. This feasibility study demonstrates the potential of an adversarial autoencoder network for correcting respiratory motion-related image artifacts without requiring paired data.
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Affiliation(s)
- Vahid Ghodrati
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
| | - Mark Bydder
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Fadil Ali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
| | - Chang Gao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
| | - Ashley Prosper
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kim-Lien Nguyen
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Peng Hu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
- Correspondence to: Peng Hu, PhD, Department of Radiological Sciences, 300 UCLA Medical Plaza Suite B119, Los Angeles, CA 90095,
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23
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Moradi F, Brunsing RL, Sheth VR, Iagaru A. Positron Emission Tomography–Magnetic Resonance Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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24
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Solomon E, Rigie DS, Vahle T, Paška J, Bollenbeck J, Sodickson DK, Boada FE, Block KT, Chandarana H. Free-breathing radial imaging using a pilot-tone radiofrequency transmitter for detection of respiratory motion. Magn Reson Med 2020; 85:2672-2685. [PMID: 33306216 DOI: 10.1002/mrm.28616] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/14/2020] [Accepted: 11/05/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To describe an approach for detection of respiratory signals using a transmitted radiofrequency (RF) reference signal called Pilot-Tone (PT) and to use the PT signal for creation of motion-resolved images based on 3D stack-of-stars imaging under free-breathing conditions. METHODS This work explores the use of a reference RF signal generated by a small RF transmitter, placed outside the MR bore. The reference signal is received in parallel to the MR signal during each readout. Because the received PT amplitude is modulated by the subject's breathing pattern, a respiratory signal can be obtained by detecting the strength of the received PT signal over time. The breathing-induced PT signal modulation can then be used for reconstructing motion-resolved images from free-breathing scans. The PT approach was tested in volunteers using a radial stack-of-stars 3D gradient echo (GRE) sequence with golden-angle acquisition. RESULTS Respiratory signals derived from the proposed PT method were compared to signals from a respiratory cushion sensor and k-space-center-based self-navigation under different breathing conditions. Moreover, the accuracy was assessed using a modified acquisition scheme replacing the golden-angle scheme by a zero-angle acquisition. Incorporating the PT signal into eXtra-Dimensional (XD) motion-resolved reconstruction led to improved image quality and clearer anatomical depiction of the lung and liver compared to k-space-center signal and motion-averaged reconstruction, when binned into 6, 8, and 10 motion states. CONCLUSION PT is a novel concept for tracking respiratory motion. Its small dimension (8 cm), high sampling rate, and minimal interaction with the imaging scan offers great potential for resolving respiratory motion.
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Affiliation(s)
- Eddy Solomon
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - David S Rigie
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | | | - Jan Paška
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | | | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Fernando E Boada
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Siemens Healthcare GmbH, Erlangen, Germany
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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25
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Cruz G, Jaubert O, Qi H, Bustin A, Milotta G, Schneider T, Koken P, Doneva M, Botnar RM, Prieto C. 3D free-breathing cardiac magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2020; 33:e4370. [PMID: 32696590 DOI: 10.1002/nbm.4370] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 05/15/2023]
Abstract
PURPOSE To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T1 and T2 mapping from a free-breathing scan. METHODS Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T1 and T2 mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T1 and T2 tissue characterization from a free-breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1 /T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2 -GraSE mapping techniques at 1.5 T. RESULTS 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1 , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T2 . CONCLUSION The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.
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Affiliation(s)
- Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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26
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Brunnquell CL, Hoff MN, Balu N, Nguyen XV, Oztek MA, Haynor DR. Making Magnets More Attractive: Physics and Engineering Contributions to Patient Comfort in MRI. Top Magn Reson Imaging 2020; 29:167-174. [PMID: 32541257 DOI: 10.1097/rmr.0000000000000246] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Patient comfort is an important factor of a successful magnetic resonance (MR) examination, and improvements in the patient's MR scanning experience can contribute to improved image quality, diagnostic accuracy, and efficiency in the radiology department, and therefore reduced cost. Magnet designs that are more open and accessible, reduced auditory noise of MR examinations, light and flexible radiofrequency (RF) coils, and faster motion-insensitive imaging techniques can all significantly improve the patient experience in MR imaging. In this work, we review the design, development, and implementation of these physics and engineering approaches to improve patient comfort.
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Affiliation(s)
- Christina L Brunnquell
- Department of Radiology, University of Washington, Seattle, WA Department of Radiology, The Ohio State University Wexler Medical Center, Columbus, OH
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27
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Correction of out-of-FOV motion artifacts using convolutional neural network. Magn Reson Imaging 2020; 71:93-102. [PMID: 32464243 DOI: 10.1016/j.mri.2020.05.004] [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: 02/18/2020] [Accepted: 05/14/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motion happens. Currently available algorithms are not able to correct for image artifacts introduced by out-of-FOV motion. The purpose of this study is to demonstrate the feasibility of incorporating convolutional neural network (CNN) derived prior image into solving the out-of-FOV motion problem. METHODS AND MATERIALS A modified U-net network was proposed to correct out-of-FOV motion artifacts by incorporating motion parameters into the loss function. A motion model based data fidelity term was applied in combination with the CNN prediction to further improve the motion correction performance. We trained the CNN on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories, and compared our algorithm to a gradient-based autofocusing (AF) algorithm in both 2D and 3D images. Additional experiment was performed to demonstrate the feasibility of transferring the networks to different dataset. We also evaluated the robustness of this algorithm by adding Gaussian noise to the motion parameters. The motion correction performance was evaluated using mean square error (NMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). RESULTS The proposed algorithm outperformed AF-based algorithm for both 2D (NMSE: 0.0066 ± 0.0009 vs 0.0141 ± 0.008, P < .01; PSNR: 29.60 ± 0.74 vs 21.71 ± 0.27, P < .01; SSIM: 0.89 ± 0.014 vs 0.73 ± 0.004, P < .01) and 3D imaging (NMSE: 0.0067 ± 0.0008 vs 0.070 ± 0.021, P < .01; PSNR: 32.40 ± 1.63 vs 22.32 ± 2.378, P < .01; SSIM: 0.89 ± 0.01 vs 0.62 ± 0.03, P < .01). Robust reconstruction was achieved with 20% data missed due to the out-of-FOV motion. CONCLUSION In conclusion, the proposed CNN-based motion correction algorithm can significantly reduce out-of-FOV motion artifacts and achieve better image quality compared to AF-based algorithm.
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28
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de Senneville BD, Cardiet CR, Trotier AJ, Ribot EJ, Lafitte L, Facq L, Miraux S. Optimizing 4D abdominal MRI: image denoising using an iterative back-projection approach. Phys Med Biol 2020; 65:015003. [PMID: 31714255 DOI: 10.1088/1361-6560/ab563e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
4D-MRI is a promising tool for organ exploration, target delineation and treatment planning. Intra-scan motion artifacts may be greatly reduced by increasing the imaging frame rate. However, poor signal-to-noise ratios (SNR) are observed when increasing spatial and/or frame number per physiological cycle, in particular in the abdomen. In the current work, the proposed 4D-MRI method favored spatial resolution, frame number, isotropic voxels and large field-of-view (FOV) during MR-acquisition. The consequential SNR penalty in the reconstructed data is addressed retrospectively using an iterative back-projection (IBP) algorithm. Practically, after computing individual spatial 3D deformations present in the images using a deformable image registration (DIR) algorithm, each 3D image is individually enhanced by fusing several successive frames in its local temporal neighborood, these latter being likely to cover common independent informations. A tuning parameter allows one to freely readjust the balance between temporal resolution and precision of the 4D-MRI. The benefit of the method was quantitatively evaluated on the thorax of 6 mice under free breathing using a clinically acceptable duration. Improved 4D cardiac imaging was also shown in the heart of 1 mice. Obtained results are compared to theoretical expectations and discussed. The proposed implementation is easily parallelizable and optimized 4D-MRI could thereby be obtained with a clinically acceptable duration.
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Affiliation(s)
- B Denis de Senneville
- 'Institut de Mathématiques de Bordeaux', University of Bordeaux/CNRS UMR 5251, 351 Cours de la Libération, 33405 Talence Cedex, France. Author to whom any correspondence should be addressed
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29
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Zhou Z, Yuan C, Börnert P. Self-calibrating wave-encoded 3D turbo spin echo imaging using subspace model based autofocusing. Magn Reson Med 2019; 83:1250-1262. [PMID: 31628767 DOI: 10.1002/mrm.28007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 08/02/2019] [Accepted: 08/31/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a self-calibrating approach for the estimation of wave point spread function (PSF) and coil sensitivities from the subsampled wave-encoded k-space, and evaluate its performance for wave-encoded 3D turbo spin echo (TSE) imaging. METHODS A low rank subspace parametric model was demonstrated in simulation to improve the representation for practical wave encoding k-space trajectories with aperiodicity, and an autofocus metric for the entire imaging volume was used to calibrate the wave PSF in a 2-stage manner from coarse to refined estimation. The coil sensitivities can be extracted from the shifted central region of wave PSF corrected subsampled k-space, and further used with wave PSF for wave-encoded parallel imaging (PI) reconstruction. The wave encoding gradients were integrated into the 3D TSE sequence considering eddy current reduction aspects and maintaining of the Carr-Purcell-Meiboom-Gill condition. Phantom and in vivo brain experiments were performed to evaluate the accuracy of wave PSF self-calibration and to compare the PI reconstruction performance between wave and Cartesian encoding scheme. RESULTS The self-calibrated wave PSF, estimated from different k-space undersampling patterns can robustly correct the wave encoding induced image artifacts given sufficient central autocalibration data. The self-calibrating wave-encoded PI reconstruction has demonstrated its improved performance in reduced aliasing artifacts and noise amplification in comparison to the Cartesian-encoded PI reconstruction results for 3D TSE imaging. CONCLUSION The proposed self-calibrating wave-encoded method allows robust calibration of wave PSF and coil sensitivities from the subsampled k-space, and improves the overall image quality for accelerated 3D TSE imaging.
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Affiliation(s)
- Zechen Zhou
- Philips Research North America, Cambridge, Massachusetts
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, Washington
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30
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Jaeschke SHF, Robson MD, Hess AT. Scattering matrix imaging pulse design for real-time respiration and cardiac motion monitoring. Magn Reson Med 2019; 82:2169-2177. [PMID: 31317579 PMCID: PMC6771869 DOI: 10.1002/mrm.27884] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 05/01/2019] [Accepted: 06/07/2019] [Indexed: 12/26/2022]
Abstract
Purpose The scattering matrix (S‐matrix) of a parallel transmit (pTx) coil is sensitive to physiological motion but requires additional monitoring RF pulses to be measured. In this work, we present and evaluate pTx RF pulse designs that simultaneously excite for imaging and measure the S‐matrix to generate real‐time motion signals without prolonging the image sequence. Theory and Methods Three pTx waveforms for measuring the S‐matrix were identified and superimposed onto the imaging excitation RF pulses: (1) time division multiplexing, (2) frequency division multiplexing, and (3) code division multiplexing. These 3 methods were evaluated in healthy volunteers for scattering sensitivity and image artefacts. The S‐matrix and real‐time motion signals were calculated on the image calculation environment of the MR scanner. Prospective cardiac triggers were identified in early systole as a high rate of change of the cardiac motion signal. Monitoring accuracy was compared against electrocardiogram or the imaged diaphragm position. Results All 3 monitoring approaches measure the S‐matrix during image excitation with quality correlated to input power. No image artefacts were observed for frequency multiplexing, and low energy artefacts were observed in the other methods. The accuracy of the achieved prospective cardiac gating was 15 ± 16 ms for breath hold and 24 ± 17 ms during free breathing. The diaphragm position prediction accuracy was 1.3 ± 0.9 mm. In all volunteers, good quality cine images were acquired for breath hold scans and dual gated CINEs were demonstrated. Conclusion The S‐matrix can be measured during image excitation to generate real‐time cardiac and respiratory motion signals for prospective gating. No artefacts are introduced when frequency division multiplexing is used.
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Affiliation(s)
- Sven H F Jaeschke
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Matthew D Robson
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.,Perspectum Diagnostics, Oxford, United Kingdom
| | - Aaron T Hess
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.,BHF Centre of Research Excellence, University of Oxford, Oxford, United Kingdom
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31
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Ippoliti M, Lukas M, Brenner W, Schaeffter T, Makowski MR, Kolbitsch C. 3D nonrigid motion correction for quantitative assessment of hepatic lesions in DCE-MRI. Magn Reson Med 2019; 82:1753-1766. [PMID: 31228296 PMCID: PMC6771884 DOI: 10.1002/mrm.27867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/03/2019] [Accepted: 05/24/2019] [Indexed: 12/27/2022]
Abstract
Purpose To provide nonrigid respiratory motion‐corrected DCE‐MRI images with isotropic resolution of 1.5 mm, full coverage of abdomen, and covering the entire uptake curve with a temporal resolution of 6 seconds, for the quantitative assessment of hepatic lesions. Methods 3D DCE‐MRI data were acquired at 3 T during free breathing for 5 minutes using a 3D T1‐weighted golden‐angle radial phase‐encoding sequence. Nonrigid respiratory motion information was extracted and used in motion‐corrected image reconstruction to obtain high‐quality DCE‐MRI images with temporal resolution of 6 seconds and isotropic resolution of 1.5 mm. An extended Tofts model was fitted to the dynamic data sets, yielding quantitative parametric maps of endothelial permeability using the hepatic artery as input function. The proposed approach was evaluated in 11 patients (52 ± 17 years, 5 men) with and without known hepatic lesions, undergoing DCE‐MRI. Results Respiratory motion produced artifacts and misalignment between dynamic volumes (lesion average motion amplitude of 3.82 ± 1.11 mm). Motion correction minimized artifacts and improved average contrast‐to‐noise ratio of hepatic lesions in late phase by 47% (p < .01). Quantitative endothelial permeability maps of motion‐corrected data demonstrated enhanced visibility of different pathologies (e.g., metastases, hemangiomas, cysts, necrotic tumor substructure) and showed improved contrast‐to‐noise ratio by 62% (p < .01) compared with uncorrected data. Conclusion 3D nonrigid motion correction in DCE‐MRI improves both visual and quantitative assessment of hepatic lesions by ensuring accurate alignment between 3D DCE images and reducing motion blurring. This approach does not require breath‐holds and minimizes scan planning by using a large FOV with isotropic resolution.
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Affiliation(s)
- Matteo Ippoliti
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Mathias Lukas
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Marcus R Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Küstner T, Armanious K, Yang J, Yang B, Schick F, Gatidis S. Retrospective correction of motion-affected MR images using deep learning frameworks. Magn Reson Med 2019; 82:1527-1540. [PMID: 31081955 DOI: 10.1002/mrm.27783] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 01/08/2023]
Abstract
PURPOSE Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion-free reacquisition can become time- and cost-intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a-priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a-priori knowledge, this problem is still challenging. METHODS We propose the use of deep learning frameworks to perform retrospective motion correction in a reference-free setting by learning from pairs of motion-free and motion-affected images. For this image-to-image translation problem, we propose and compare a variational auto encoder and generative adversarial network. Feasibility and influences of motion type and optimal architecture are investigated by blinded subjective image quality assessment and by quantitative image similarity metrics. RESULTS We observed that generative adversarial network-based motion correction is feasible producing near-realistic motion-free images as confirmed by blinded subjective image quality assessment. Generative adversarial network-based motion correction accordingly resulted in images with high evaluation metrics (normalized root mean squared error <0.08, structural similarity index >0.8, normalized mutual information >0.9). CONCLUSION Deep learning-based retrospective restoration of motion artifacts is feasible resulting in near-realistic motion-free images. However, the image translation task can alter or hide anatomical features and, therefore, the clinical applicability of this technique has to be evaluated in future studies.
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Affiliation(s)
- Thomas Küstner
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.,Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany.,School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
| | - Karim Armanious
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.,Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Jiahuan Yang
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Bin Yang
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Fritz Schick
- Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Sergios Gatidis
- Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
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Tamada D, Kromrey ML, Ichikawa S, Onishi H, Motosugi U. Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver. Magn Reson Med Sci 2019; 19:64-76. [PMID: 31061259 PMCID: PMC7067907 DOI: 10.2463/mrms.mp.2018-0156] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Purpose: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach. Materials and Methods: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T1-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots. Results: The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images. Conclusion: A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.
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Affiliation(s)
- Daiki Tamada
- Department of Radiology, University of Yamanashi
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Taso M, Zhao L, Guidon A, Litwiller DV, Alsop DC. Volumetric abdominal perfusion measurement using a pseudo-randomly sampled 3D fast-spin-echo (FSE) arterial spin labeling (ASL) sequence and compressed sensing reconstruction. Magn Reson Med 2019; 82:680-692. [PMID: 30953396 DOI: 10.1002/mrm.27761] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/04/2019] [Accepted: 03/11/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE To improve image quality and spatial coverage for abdominal perfusion imaging by implementing an arterial spin labeling (ASL) sequence that combines variable-density 3D fast-spin-echo (FSE) with Cartesian trajectory and compressed-sensing (CS) reconstruction. METHODS A volumetric FSE sequence was modified to include background-suppressed pseudo-continuous ASL labeling and to support variable-density (VD) Poisson-disk sampling for acceleration. We additionally explored the benefits of center oversampling and variable outer k-space sampling. Fourteen healthy volunteers were scanned on a 3T scanner to test acceleration factors as well as the various sampling schemes described under synchronized-breathing to limit motion issues. A CS reconstruction was implemented using the BART toolbox to reconstruct perfusion-weighted ASL volumes, assessing the impact of acceleration, different reconstruction, and sampling strategies on image quality. RESULTS CS acceleration is feasible with ASL, and a strong renal perfusion signal could be observed even at very high acceleration rates (≈15). We have shown that ASL k-space complex subtraction was desirable before CS reconstruction. Although averaging of multiple highly accelerated images helped to reduce artifacts from physiologic fluctuations, superior image quality was achieved by interleaving of different highly undersampled pseudo-random spatial sampling patterns and using 4D-CS reconstruction. Combination of these enhancements produces high-quality ASL volumes in under 5 min. CONCLUSIONS High-quality isotropic ASL abdominal perfusion volumes can be obtained in healthy volunteers with a VD-FSE and CS reconstruction. This lays the groundwork for future developments toward whole abdomen free-breathing non-contrast perfusion imaging.
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Affiliation(s)
- Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Li Zhao
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Arnaud Guidon
- Global MR applications and Workflow, GE Healthcare, Boston, Massachusetts
| | - Daniel V Litwiller
- Global MR applications and Workflow, GE Healthcare, New York City, New York
| | - David C Alsop
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
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Cao X, Ye H, Liao C, Li Q, He H, Zhong J. Fast 3D brain MR fingerprinting based on multi-axis spiral projection trajectory. Magn Reson Med 2019; 82:289-301. [PMID: 30883867 DOI: 10.1002/mrm.27726] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 02/09/2019] [Accepted: 02/12/2019] [Indexed: 01/12/2023]
Abstract
PURPOSE To develop a fast, sub-millimeter 3D magnetic resonance fingerprinting (MRF) technique for whole-brain quantitative scans. METHODS An acquisition trajectory based on multi-axis spiral projection imaging (maSPI) was implemented for 3D MRF with steady-state precession and slab excitation. By appropriately assigning the in-plane and through-plane rotations of spiral interleaves in a novel acquisition scheme, an maSPI-based acquisition was implemented, and the total acquisition time was reduced by up to a factor of 8 compared to stack-of-spiral (SOS)-based acquisition. A sliding-window method was also used to further reduce the required number of time points for a faster acquisition. The experiments were conducted both on a phantom and in vivo. RESULTS The results from the phantom measurements with the proposed and gold standard methods were consistent with a good linear correlation and an R2 value approaching 0.99. The in vivo experiments achieved whole-brain parametric maps with isotropic resolutions of 1 mm and 0.8 mm in 5.0 and 6.0 min, respectively, with potential for further acceleration. An in vivo experiment with intentionally moving subjects demonstrated that the maSPI scheme largely outperforms the SOS scheme in terms of robustness to head motion. CONCLUSION 3D MRF with an maSPI acquisition scheme enables fast and robust scans for high-resolution parametric mapping.
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Affiliation(s)
- Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Huihui Ye
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.,State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Congyu Liao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York
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Cruz G, Jaubert O, Schneider T, Botnar RM, Prieto C. Rigid motion-corrected magnetic resonance fingerprinting. Magn Reson Med 2019; 81:947-961. [PMID: 30229558 PMCID: PMC6519164 DOI: 10.1002/mrm.27448] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 06/06/2018] [Accepted: 06/13/2018] [Indexed: 12/30/2022]
Abstract
PURPOSE Develop a method for rigid body motion-corrected magnetic resonance fingerprinting (MRF). METHODS MRF has shown some robustness to abrupt motion toward the end of the acquisition. Here, we study the effects of different types of rigid body motion during the acquisition on MRF and propose a novel approach to correct for this motion. The proposed method (MC-MRF) follows 4 steps: (1) sliding window reconstruction is performed to produce high-quality auxiliary dynamic images; (2) rotation and translation motion is estimated from the dynamic images by image registration; (3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase shifts; and (4) motion-corrected data are reconstructed with low-rank inversion. MC-MRF was validated in a standard T1 /T2 phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through-plane motion in 2D MC-MRF was investigated. RESULTS Simulation results show that motion in MRF can introduce artifacts in T1 and T2 maps, depending when it occurs. MC-MRF improved parametric map quality in all phantom and in vivo experiments with in-plane motion, comparable to the no-motion ground truth. Reduced parametric map quality, even after motion correction, was observed for acquisitions with through-plane motion, particularly for smaller structures in T2 maps. CONCLUSION Here, a novel method for motion correction in MRF (MC-MRF) is proposed, which improves parametric map quality and accuracy in comparison to no-motion correction approaches. Future work will include validation of 3D MC-MRF to enable also through-plane motion correction.
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Affiliation(s)
- Gastão Cruz
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Olivier Jaubert
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | | | - Rene M. Botnar
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Católica de Chile, Escuela de IngenieríaSantiagoChile
| | - Claudia Prieto
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Católica de Chile, Escuela de IngenieríaSantiagoChile
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Barkovich MJ, Li Y, Desikan RS, Barkovich AJ, Xu D. Challenges in pediatric neuroimaging. Neuroimage 2019; 185:793-801. [PMID: 29684645 PMCID: PMC6197938 DOI: 10.1016/j.neuroimage.2018.04.044] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 04/13/2018] [Accepted: 04/19/2018] [Indexed: 02/04/2023] Open
Abstract
Pediatric neuroimaging is challenging due the rapid structural, metabolic, and functional changes that occur in the developing brain. A specially trained team is needed to produce high quality diagnostic images in children, due to their small physical size and immaturity. Patient motion, cooperation and medical condition dictate the methods and equipment used. A customized approach tailored to each child's age and functional status with the appropriate combination of dedicated staff, imaging hardware, and software is key; these range from low-tech techniques, such as feed and swaddle, to specialized small bore MRI scanners, MRI compatible incubators and neonatal head coils. New pre-and post-processing techniques can also compensate for the motion artifacts and low signal that often degrade neonatal scans.
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Affiliation(s)
- Matthew J Barkovich
- Department of Radiology and Diagnostic Imaging, University of California, San Francisco 505 Parnassus Avenue, Room L352, San Francisco, CA 94143-0628, United States.
| | - Yi Li
- Department of Radiology and Diagnostic Imaging, University of California, San Francisco 505 Parnassus Avenue, Room L352, San Francisco, CA 94143-0628, United States
| | - Rahul S Desikan
- Department of Radiology and Diagnostic Imaging, University of California, San Francisco 505 Parnassus Avenue, Room L352, San Francisco, CA 94143-0628, United States
| | - A James Barkovich
- Department of Radiology and Diagnostic Imaging, University of California, San Francisco 505 Parnassus Avenue, Room L352, San Francisco, CA 94143-0628, United States; UCSF-Benioff Children's Hospital, 1975 4th St, San Francisco, CA 94158, United States
| | - Duan Xu
- Department of Radiology and Diagnostic Imaging, University of California, San Francisco 505 Parnassus Avenue, Room L352, San Francisco, CA 94143-0628, United States; UCSF-Benioff Children's Hospital, 1975 4th St, San Francisco, CA 94158, United States
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Rigie D, Vahle T, Zhao T, Czekella B, Frohwein LJ, Schäfers K, Boada FE. Cardiorespiratory motion-tracking via self-refocused rosette navigators. Magn Reson Med 2019; 81:2947-2958. [PMID: 30615208 DOI: 10.1002/mrm.27609] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/27/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a flexible method for tracking respiratory and cardiac motions throughout MR and PET-MR body examinations that requires no additional hardware and minimal sequence modification. METHODS The incorporation of a contrast-neutral rosette navigator module following the RF excitation allows for robust cardiorespiratory motion tracking with minimal impact on the host sequence. Spatial encoding gradients are applied to the FID signal and the desired motion signals are extracted with a blind source separation technique. This approach is validated with an anthropomorphic, PET-MR-compatible motion phantom as well as in 13 human subjects. RESULTS Both respiratory and cardiac motions were reliably extracted from the proposed rosette navigator in phantom and patient studies. In the phantom study, the MR-derived motion signals were additionally validated against the ground truth measurement of diaphragm displacement and left ventricle model triggering pulse. CONCLUSION The proposed method yields accurate respiratory and cardiac motion-state tracking, requiring only a short (1.76 ms) additional navigator module, which is self-refocusing and imposes minimal constraints on sequence design.
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Affiliation(s)
- David Rigie
- Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, New York
| | | | - Tiejun Zhao
- Siemens Medical Solutions, New York, New York
| | - Björn Czekella
- European Institute for Molecular Imaging, Münster, Germany
| | | | - Klaus Schäfers
- European Institute for Molecular Imaging, Münster, Germany
| | - Fernando E Boada
- Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, New York
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Weller DS, Wang L, Mugler JP, Meyer CH. Motion-compensated reconstruction of magnetic resonance images from undersampled data. Magn Reson Imaging 2019; 55:36-45. [PMID: 30213754 PMCID: PMC6242755 DOI: 10.1016/j.mri.2018.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 08/16/2018] [Accepted: 09/08/2018] [Indexed: 02/03/2023]
Abstract
Magnetic resonance imaging of patients who find difficulty lying still or holding their breath can be challenging. Unresolved intra-frame motion yields blurring artifacts and limits spatial resolution. To correct for intra-frame non-rigid motion, such as in pediatric body imaging, this paper describes a multi-scale technique for joint estimation of the motion occurring during the acquisition and of the desired uncorrupted image. This technique regularizes the motion coefficients to enforce invertibility and minimize numerical instability. This multi-scale approach takes advantage of variable-density sampling patterns used in accelerated imaging to resolve large motion from a coarse scale. The resulting method improves image quality for a set of two-dimensional reconstructions from data simulated with independently generated deformations, with statistically significant increases in both peak signal to error ratio and structural similarity index. These improvements are consistent across varying undersampling factors and severities of motion and take advantage of the variable density sampling pattern.
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Affiliation(s)
| | - Luonan Wang
- University of Virginia, Charlottesville, VA 22904, USA.
| | - John P Mugler
- University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
| | - Craig H Meyer
- University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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Johansson A, Balter JM, Cao Y. Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Med Phys 2018; 45:4529-4540. [PMID: 30098044 DOI: 10.1002/mp.13118] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Revised: 07/29/2018] [Accepted: 07/29/2018] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Abdominal dynamic contrast-enhanced (DCE) MRI suffers from motion-induced artifacts that can blur images and distort contrast-agent uptake curves. For liver perfusion analysis, image reconstruction with rigid-body motion correction (RMC) can restore distorted portal-venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing-induced deformation in the whole abdomen. METHODS Raw data from a golden-angle stack-of-stars gradient-echo sequence were collected for 54 DCE-MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end-exhale state. Resulting deformation fields were used to correct back-projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC. RESULTS DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal-venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients. CONCLUSIONS DCE-MRI reconstruction with DMC can restore motion-distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC.
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Affiliation(s)
- Adam Johansson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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Haskell MW, Cauley SF, Wald LL. TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1253-1265. [PMID: 29727288 PMCID: PMC6633918 DOI: 10.1109/tmi.2018.2791482] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search. With this strategy we are able to effectively capture the tight coupling between the image voxel values and motion parameters. We demonstrate in simulations TAMER's ability to find similar search directions compared to a full model, with an average error of 22%, vs. 73% error when using previously proposed alternating methods. The reduced model decreased the computation time fold compared to a full image volume evaluation. In phantom experiments, our method successfully mitigates both translation and rotation artifacts, reducing image RMSE compared to a motion-free gold standard from 21% to 14% in a translating phantom, and from 17% to 10% in a rotating phantom. Qualitative image improvements are seen in human imaging of moving subjects compared to conventional reconstruction. Finally, we compare in vivo image results of our method to the state-of-the-art.
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Chen F, Zhang T, Cheng JY, Shi X, Pauly JM, Vasanawala SS. Autocalibrating motion-corrected wave-encoding for highly accelerated free-breathing abdominal MRI. Magn Reson Med 2017; 78:1757-1766. [PMID: 27943402 PMCID: PMC5466545 DOI: 10.1002/mrm.26567] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Revised: 10/26/2016] [Accepted: 11/10/2016] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a motion-robust wave-encoding technique for highly accelerated free-breathing abdominal MRI. METHODS A comprehensive 3D wave-encoding-based method was developed to enable fast free-breathing abdominal imaging: (a) auto-calibration for wave-encoding was designed to avoid extra scan for coil sensitivity measurement; (b) intrinsic butterfly navigators were used to track respiratory motion; (c) variable-density sampling was included to enable compressed sensing; (d) golden-angle radial-Cartesian hybrid view-ordering was incorporated to improve motion robustness; and (e) localized rigid motion correction was combined with parallel imaging compressed sensing reconstruction to reconstruct the highly accelerated wave-encoded datasets. The proposed method was tested on six subjects and image quality was compared with standard accelerated Cartesian acquisition both with and without respiratory triggering. Inverse gradient entropy and normalized gradient squared metrics were calculated, testing whether image quality was improved using paired t-tests. RESULTS For respiratory-triggered scans, wave-encoding significantly reduced residual aliasing and blurring compared with standard Cartesian acquisition (metrics suggesting P < 0.05). For non-respiratory-triggered scans, the proposed method yielded significantly better motion correction compared with standard motion-corrected Cartesian acquisition (metrics suggesting P < 0.01). CONCLUSION The proposed methods can reduce motion artifacts and improve overall image quality of highly accelerated free-breathing abdominal MRI. Magn Reson Med 78:1757-1766, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Feiyu Chen
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Tao Zhang
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Joseph Y. Cheng
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Xinwei Shi
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - John M. Pauly
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Jiang W, Ong F, Johnson KM, Nagle SK, Hope TA, Lustig M, Larson PEZ. Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator. Magn Reson Med 2017; 79:2954-2967. [PMID: 29023975 DOI: 10.1002/mrm.26958] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 08/16/2017] [Accepted: 09/14/2017] [Indexed: 01/01/2023]
Abstract
PURPOSE To achieve motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data. METHODS Five-minute free-breathing scans were acquired with a 3D ultrashort echo time (UTE) sequence with 1.25 mm isotropic resolution. From this data, dynamic 3D self-navigating images were reconstructed under locally low rank (LLR) constraints and used for motion compensation with one of two methods: a soft-gating technique to penalize the respiratory motion induced data inconsistency, and a respiratory motion-resolved technique to provide images of all respiratory motion states. RESULTS Respiratory motion estimation derived from the proposed dynamic 3D self-navigator of 7.5 mm isotropic reconstruction resolution and a temporal resolution of 300 ms was successful for estimating complex respiratory motion patterns. This estimation improved image quality compared to respiratory belt and DC-based navigators. Respiratory motion compensation with soft-gating and respiratory motion-resolved techniques provided good image quality from highly undersampled data in volunteers and clinical patients. CONCLUSION An optimized 3D UTE sequence combined with the proposed reconstruction methods can provide high-resolution motion robust pulmonary MRI. Feasibility was shown in patients who had irregular breathing patterns in which our approach could depict clinically relevant pulmonary pathologies. Magn Reson Med 79:2954-2967, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Wenwen Jiang
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA
| | - Frank Ong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin, Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin, Madison, Madison, Wisconsin, USA
| | - Scott K Nagle
- Department of Medical Physics, University of Wisconsin, Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin, Madison, Madison, Wisconsin, USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Michael Lustig
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Peder E Z Larson
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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Hess AT, Tunnicliffe EM, Rodgers CT, Robson MD. Diaphragm position can be accurately estimated from the scattering of a parallel transmit RF coil at 7 T. Magn Reson Med 2017; 79:2164-2169. [PMID: 28771792 PMCID: PMC5836958 DOI: 10.1002/mrm.26866] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 07/12/2017] [Accepted: 07/19/2017] [Indexed: 11/10/2022]
Abstract
Purpose To evaluate the use of radiofrequency scattering of a parallel transmit coil to track diaphragm motion. Methods Measurements made during radiofrequency excitation on an 8‐channel parallel transmit coil by the directional couplers of the radiofrequency safety monitor were combined and converted into diaphragm position. A 30‐s subject‐specific calibration with an MRI navigator was used to determine a diaphragm estimate from each directional‐coupler measure. Seven healthy volunteers were scanned at 7 T, in which images of the diaphragm were continuously acquired and directional couplers were monitored during excitation radiofrequency pulses. The ability to detect coughing was evaluated in one subject. The method was implemented on the scanner and evaluated for diaphragm gating of a free‐breathing cardiac cine. Results Six of the seven scans were successful. In these subjects, the root mean square difference between MRI and scattering estimation of the superior–inferior diaphragm position was 1.4 ± 0.5 mm. On the scanner, the position was calculated less than 2 ms after every radiofrequency pulse. A prospectively gated (echocardiogram and respiration) high‐resolution free‐breathing cine showed no respiratory artifact and sharp blood‐myocardium definition. Conclusions Transmit coil scattering is sensitive to diaphragm motion and provides rapid, quantitative, and accurate monitoring of respiration. Magn Reson Med 79:2164–2169, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Aaron T Hess
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Elizabeth M Tunnicliffe
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Christopher T Rodgers
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Matthew D Robson
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
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Küstner T, Schwartz M, Martirosian P, Gatidis S, Seith F, Gilliam C, Blu T, Fayad H, Visvikis D, Schick F, Yang B, Schmidt H, Schwenzer NF. MR-based respiratory and cardiac motion correction for PET imaging. Med Image Anal 2017; 42:129-144. [PMID: 28800546 DOI: 10.1016/j.media.2017.08.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 07/18/2017] [Accepted: 08/01/2017] [Indexed: 01/22/2023]
Abstract
PURPOSE To develop a motion correction for Positron-Emission-Tomography (PET) using simultaneously acquired magnetic-resonance (MR) images within 90 s. METHODS A 90 s MR acquisition allows the generation of a cardiac and respiratory motion model of the body trunk. Thereafter, further diagnostic MR sequences can be recorded during the PET examination without any limitation. To provide full PET scan time coverage, a sensor fusion approach maps external motion signals (respiratory belt, ECG-derived respiration signal) to a complete surrogate signal on which the retrospective data binning is performed. A joint Compressed Sensing reconstruction and motion estimation of the subsampled data provides motion-resolved MR images (respiratory + cardiac). A 1-POINT DIXON method is applied to these MR images to derive a motion-resolved attenuation map. The motion model and the attenuation map are fed to the Customizable and Advanced Software for Tomographic Reconstruction (CASToR) PET reconstruction system in which the motion correction is incorporated. All reconstruction steps are performed online on the scanner via Gadgetron to provide a clinically feasible setup for improved general applicability. The method was evaluated on 36 patients with suspected liver or lung metastasis in terms of lesion quantification (SUVmax, SNR, contrast), delineation (FWHM, slope steepness) and diagnostic confidence level (3-point Likert-scale). RESULTS A motion correction could be conducted for all patients, however, only in 30 patients moving lesions could be observed. For the examined 134 malignant lesions, an average improvement in lesion quantification of 22%, delineation of 64% and diagnostic confidence level of 23% was achieved. CONCLUSION The proposed method provides a clinically feasible setup for respiratory and cardiac motion correction of PET data by simultaneous short-term MRI. The acquisition sequence and all reconstruction steps are publicly available to foster multi-center studies and various motion correction scenarios.
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Affiliation(s)
- Thomas Küstner
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany; Department of Radiology, University of Tübingen, Tübingen, Germany.
| | - Martin Schwartz
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany; Section on Experimental Radiology, University of Tübingen, Germany
| | | | - Sergios Gatidis
- Department of Radiology, University of Tübingen, Tübingen, Germany
| | - Ferdinand Seith
- Department of Radiology, University of Tübingen, Tübingen, Germany
| | - Christopher Gilliam
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong
| | - Thierry Blu
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong
| | - Hadi Fayad
- INSERM U1101, LaTIM, University of Bretagne, Brest, France
| | | | - F Schick
- Section on Experimental Radiology, University of Tübingen, Germany
| | - B Yang
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - H Schmidt
- Department of Radiology, University of Tübingen, Tübingen, Germany
| | - N F Schwenzer
- Department of Radiology, University of Tübingen, Tübingen, Germany
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Comprehensive Multi-Dimensional MRI for the Simultaneous Assessment of Cardiopulmonary Anatomy and Physiology. Sci Rep 2017; 7:5330. [PMID: 28706270 PMCID: PMC5509743 DOI: 10.1038/s41598-017-04676-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 05/18/2017] [Indexed: 01/22/2023] Open
Abstract
Diagnostic testing often assesses the cardiovascular or respiratory systems in isolation, ignoring the major pathophysiologic interactions between the systems in many diseases. When both systems are assessed currently, multiple modalities are utilized in costly fashion with burdensome logistics and decreased accessibility. Thus, we have developed a new acquisition and reconstruction paradigm using the flexibility of MRI to enable a comprehensive exam from a single 5-15 min scan. We constructed a compressive-sensing approach to pseudo-randomly acquire highly subsampled, multi-dimensionally-encoded and time-stamped data from which we reconstruct volumetric cardiac and respiratory motion phases, contrast-agent dynamics, and blood flow velocity fields. The proposed method, named XD flow, is demonstrated for (a) evaluating congenital heart disease, where the impact of bulk motion is reduced in a non-sedated neonatal patient and (b) where the observation of the impact of respiration on flow is necessary for diagnostics; (c) cardiopulmonary imaging, where cardiovascular flow, function, and anatomy information is needed along with pulmonary perfusion quantification; and in (d) renal function imaging, where blood velocities and glomerular filtration rates are simultaneously measured, which highlights the generality of the technique. XD flow has the ability to improve quantification and to provide additional data for patient diagnosis for comprehensive evaluations.
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Kolbitsch C, Neji R, Fenchel M, Mallia A, Marsden P, Schaeffter T. Fully integrated 3D high-resolution multicontrast abdominal PET-MR with high scan efficiency. Magn Reson Med 2017; 79:900-911. [DOI: 10.1002/mrm.26757] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 03/29/2017] [Accepted: 04/22/2017] [Indexed: 12/19/2022]
Affiliation(s)
- Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB); Braunschweig and Berlin Germany
- King's College London, Division of Imaging Sciences and Biomedical Engineering; London UK
| | - Radhouene Neji
- MR Research Collaborations, Siemens Healthcare; Frimley UK
| | - Matthias Fenchel
- MR Oncology Application Development, Siemens Healthcare; Erlangen Germany
| | - Andrew Mallia
- King's College London, Division of Imaging Sciences and Biomedical Engineering; London UK
| | - Paul Marsden
- King's College London, Division of Imaging Sciences and Biomedical Engineering; London UK
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB); Braunschweig and Berlin Germany
- King's College London, Division of Imaging Sciences and Biomedical Engineering; London UK
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48
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Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. Compressed sensing for body MRI. J Magn Reson Imaging 2016; 45:966-987. [PMID: 27981664 DOI: 10.1002/jmri.25547] [Citation(s) in RCA: 185] [Impact Index Per Article: 23.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/25/2016] [Indexed: 12/18/2022] Open
Abstract
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2017;45:966-987.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Tavallaei MA, Johnson PM, Liu J, Drangova M. Design and evaluation of an MRI-compatible linear motion stage. Med Phys 2016; 43:62. [PMID: 26745900 DOI: 10.1118/1.4937780] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To develop and evaluate a tool for accurate, reproducible, and programmable motion control of imaging phantoms for use in motion sensitive magnetic resonance imaging (MRI) appli cations. METHODS In this paper, the authors introduce a compact linear motion stage that is made of nonmagnetic material and is actuated with an ultrasonic motor. The stage can be positioned at arbitrary positions and orientations inside the scanner bore to move, push, or pull arbitrary phantoms. Using optical trackers, measuring microscopes, and navigators, the accuracy of the stage in motion control was evaluated. Also, the effect of the stage on image signal-to-noise ratio (SNR), artifacts, and B0 field homogeneity was evaluated. RESULTS The error of the stage in reaching fixed positions was 0.025 ± 0.021 mm. In execution of dynamic motion profiles, the worst-case normalized root mean squared error was below 7% (for frequencies below 0.33 Hz). Experiments demonstrated that the stage did not introduce artifacts nor did it degrade the image SNR. The effect of the stage on the B0 field was less than 2 ppm. CONCLUSIONS The results of the experiments indicate that the proposed system is MRI-compatible and can create reliable and reproducible motion that may be used for validation and assessment of motion related MRI applications.
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Affiliation(s)
- Mohammad Ali Tavallaei
- Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada and Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario N6A 5B9, Canada
| | - Patricia M Johnson
- Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada and Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario N6A 5C1, Canada
| | - Junmin Liu
- Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada
| | - Maria Drangova
- Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario N6A 5B9, Canada; and Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario N6A 5C1, Canada
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50
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Self-navigated 4D cartesian imaging of periodic motion in the body trunk using partial k-space compressed sensing. Magn Reson Med 2016; 78:632-644. [DOI: 10.1002/mrm.26406] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 08/04/2016] [Accepted: 08/10/2016] [Indexed: 12/28/2022]
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