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Kleineisel J, Heidenreich JF, Eirich P, Petri N, Köstler H, Petritsch B, Bley TA, Wech T. Real-time cardiac MRI using an undersampled spiral k-space trajectory and a reconstruction based on a variational network. Magn Reson Med 2022; 88:2167-2178. [PMID: 35692042 DOI: 10.1002/mrm.29357] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 05/17/2022] [Accepted: 05/20/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time-consuming and can lead to compromised image quality in the case of arrhythmia or dyspnea. In this article, a machine learning-based reconstruction of undersampled spiral k-space data is presented to enable free breathing real-time cardiac MRI with good image quality and short reconstruction times. METHODS Data were acquired in free breathing with a 2D spiral trajectory corrected by the gradient system transfer function. Undersampled data were reconstructed by a variational network (VN), which was specifically adapted to the non-Cartesian sampling pattern. The network was trained with data from 11 subjects. Subsequently, the imaging technique was validated in 14 subjects by quantifying the difference to a segmented reference acquisition, an expert reader study, and by comparing derived volumes and functional parameters with values obtained using the current clinical gold standard. RESULTS The scan time for the entire heart was below 1 min. The VN reconstructed data in about 0.9 s per image, which is considerably shorter than conventional model-based approaches. The VN furthermore performed better than a U-Net and not inferior to a low-rank plus sparse model in terms of achieved image quality. Functional parameters agreed, on average, with reference data. CONCLUSIONS The proposed VN method enables real-time cardiac imaging with both high spatial and temporal resolution in free breathing and with short reconstruction time.
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Affiliation(s)
- Jonas Kleineisel
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Julius F Heidenreich
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Philipp Eirich
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.,Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany
| | - Nils Petri
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Herbert Köstler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Bernhard Petritsch
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Thorsten A Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
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Nita N, Kersten J, Pott A, Weber F, Tesfay T, Benea MT, Metze P, Li H, Rottbauer W, Rasche V, Buckert D. Real-Time Spiral CMR Is Superior to Conventional Segmented Cine-Imaging for Left-Ventricular Functional Assessment in Patients with Arrhythmia. J Clin Med 2022; 11:jcm11082088. [PMID: 35456181 PMCID: PMC9025940 DOI: 10.3390/jcm11082088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/29/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Segmented Cartesian Cardiovascular magnetic resonance (CMR) often fails to deliver robust assessment of cardiac function in patients with arrhythmia. We aimed to assess the performance of a tiny golden-angle spiral real-time CMR sequence at 1.5 T for left-ventricular (LV) volumetry in patients with irregular heart rhythm; (2) Methods: We validated the real-time sequence against the standard breath-hold segmented Cartesian sequence in 32 patients, of whom 11 presented with arrhythmia. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) were assessed. In arrhythmic patients, real-time and standard Cartesian acquisitions were compared against a reference echocardiographic modality; (3) Results: In patients with sinus rhythm, good agreements and correlations were found between the segmented and real-time methods, with only minor, non-significant underestimation of EDV for the real-time sequence (135.95 ± 30 mL vs. 137.15 ± 31, p = 0.164). In patients with arrhythmia, spiral real-time CMR yielded superior image quality to the conventional segmented imaging, allowing for excellent agreement with the reference echocardiographic volumetry. In contrast, in this cohort, standard Cartesian CMR showed significant underestimation of LV-ESV (106.72 ± 63.51 mL vs. 125.47 ± 72.41 mL, p = 0.026) and overestimation of LVEF (42.96 ± 10.81% vs. 39.02 ± 11.72%, p = 0.039); (4) Conclusions: Real-time spiral CMR improves image quality in arrhythmic patients, allowing reliable assessment of LV volumetry.
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Affiliation(s)
- Nicoleta Nita
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
- Correspondence:
| | - Johannes Kersten
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Alexander Pott
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Fabian Weber
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Temsgen Tesfay
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | | | - Patrick Metze
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Hao Li
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Wolfgang Rottbauer
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Volker Rasche
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
| | - Dominik Buckert
- Department of Internal Medicine II, University Medical Center, 89081 Ulm, Germany; (J.K.); (A.P.); (F.W.); (T.T.); (P.M.); (H.L.); (W.R.); (V.R.); (D.B.)
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Li YY, Rashid S, Craft J, Cheng YJ, Schapiro W, Gliganic K, Yamashita AM, Grgas M, Haag E, Cao JJ. Temporospatial characterization of ventricular wall motion with real-time cardiac magnetic resonance imaging in health and disease. Sci Rep 2022; 12:4070. [PMID: 35260729 DOI: 10.1038/s41598-022-08094-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 02/21/2022] [Indexed: 11/11/2022] Open
Abstract
Cardiac magnetic resonance imaging (MRI) has been largely dependent on retrospective cine for data acquisition. Real-time imaging, although inferior in image quality to retrospective cine, is more informative about motion dynamics. We herein developed a real-time cardiac MRI approach to temporospatial characterization of left ventricle (LV) and right ventricle (RV) wall motion. This approach provided two temporospatial indices, temporal periodicity and spatial coherence, for quantitative assessment of ventricular function. In a cardiac MRI study, we prospectively investigated temporospatial characterization in reference to standard volumetric measurements with retrospective cine. The temporospatial indices were found to be effective for evaluating the difference of ventricular performance between the healthy volunteers and the heart failure (HF) patients (LV temporal periodicity 0.24 ± 0.037 vs. 0.14 ± 0.021; RV temporal periodicity 0.18 ± 0.030 vs. 0.10 ± 0.014; LV spatial coherence 0.52 ± 0.039 vs. 0.38 ± 0.040; RV spatial coherence 0.50 ± 0.036 vs. 0.35 ± 0.035; all in arbitrary unit). The HF patients and healthy volunteers were well differentiated in the scatter plots of spatial coherence and temporal periodicity while they were mixed in those of end-systolic volume (ESV) and ejection fraction (EF) from volumetric measurements. This study demonstrated the potential of real-time cardiac MRI for intricate analysis of ventricular function beyond retrospective cine.
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Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
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Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
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Li YY, Craft J, Cheng Y, Schapiro W, Gliganic K, Haag E, Cao JJ. Optical Flow Analysis of Left Ventricle Wall Motion with Real-Time Cardiac Magnetic Resonance Imaging in Healthy Subjects and Heart Failure Patients. Ann Biomed Eng 2022; 50:195-210. [PMID: 35022866 DOI: 10.1007/s10439-022-02907-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 01/01/2022] [Indexed: 11/27/2022]
Abstract
In cardiology, magnetic resonance imaging (MRI) provides a clinical standard for measuring ventricular volumes. Owing to their reliability, volumetric measurements with cardiac MRI have become an essential tool for quantitative assessment of ventricular function. However, as volumetric indices are indirectly related to myocardial motion that drives ventricular filling and ejection, cardiac MRI cannot provide comprehensive evaluation of ventricular performance. To overcome this limitation, the presented work sought to measure ventricular wall motion directly with optical flow analysis of real-time cardiac MRI. By modeling left ventricle (LV) walls in real-time images based on myocardial architecture, we developed an optical flow approach to analyzing LV radial and circumferential wall motion for improved quantitative assessment of ventricular function. For proof-of-concept, a cardiac MRI study was conducted with healthy volunteers and heart failure (HF) patients. It was found that, as real-time images provided sufficient temporal information for correlation analysis between different LV wall motion velocity components, optical flow assessment detected the difference of ventricular performance between the HF patients and the healthy volunteers more effectively than volumetric measurements. We expect that this model-based optical flow assessment with real-time cardiac MRI would offer intricate analysis of ventricular function beyond conventional volumetric measurements.
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Affiliation(s)
- Yu Y Li
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA. .,Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA.
| | - Jason Craft
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - Yang Cheng
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - William Schapiro
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - Kathleen Gliganic
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - Elizabeth Haag
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - J Jane Cao
- Department of Cardiac Imaging, St. Francis Hospital, DeMatteis Center for Research and Education, 101 Northern Blvd, Greenvale, NY, 11548, USA.,Department of Clinical Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
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Nayak KS, Lim Y, Campbell-Washburn AE, Steeden J. Real-Time Magnetic Resonance Imaging. J Magn Reson Imaging 2022; 55:81-99. [PMID: 33295674 PMCID: PMC8435094 DOI: 10.1002/jmri.27411] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 01/03/2023] Open
Abstract
Real-time magnetic resonance imaging (RT-MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast-switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady-state free precession, and single-shot rapid acquisition with relaxation enhancement. RT-MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft-tissue contrast, as well as flow information. In this review, we discuss the history of RT-MRI, fundamental tradeoffs, enabling technology, established applications, and current trends. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Krishna S. Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA,Address reprint requests to: K.S.N., 3740 McClintock Ave, EEB 400C, Los Angeles, CA 90089-2564, USA.
| | - Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Adrienne E. Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer Steeden
- Institute of Cardiovascular Science, Centre for Cardiovascular Imaging, University College London, London, UK
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Rich A, Gregg M, Jin N, Liu Y, Potter L, Simonetti O, Ahmad R. CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA). Magn Reson Med 2020; 83:2015-2025. [PMID: 31721303 PMCID: PMC7059985 DOI: 10.1002/mrm.28059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 08/30/2019] [Accepted: 10/10/2019] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop a variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution for dynamic MRI applications and to validate it in real-time phase contrast MRI (PC-MRI). THEORY AND METHODS The proposed method, called CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA), begins by producing a sequence of phase encoding indices based on the golden ratio increment. Then, variable density is introduced by nonlinear stretching of the indices. Finally, the elements of the resulting sequence are rounded up to the nearest integer. The performance of CAVA is evaluated using PC-MRI data from a pulsatile flow phantom and real-time, free-breathing data from ten healthy volunteers. RESULTS CAVA enabled image recovery at various temporal resolutions that were selected retrospectively. For the pulsatile flow phantom, image quality and flow quantification accuracy from CAVA were comparable to that from another pseudo-random sampling pattern with fixed temporal resolution. In addition, flow quantification results based on CAVA were in good agreement with a breath-held segmented acquisition. CONCLUSIONS By allowing retrospective binning of the MRI data, CAVA provides an avenue to retrospectively adjust the temporal resolution of PC-MRI.
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Affiliation(s)
- Adam Rich
- Biomedical Engineering, The Ohio State University, Columbus OH, USA
| | - Michael Gregg
- Biomedical Engineering, The Ohio State University, Columbus OH, USA
- Electrical and Computer Engineering, The Ohio State University, Columbus OH, USA
| | - Ning Jin
- Cardiovascular MR R&D, Siemens Medical Solutions USA Inc., Columbus OH USA
| | - Yingmin Liu
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus OH, USA
| | - Lee Potter
- Electrical and Computer Engineering, The Ohio State University, Columbus OH, USA
| | - Orlando Simonetti
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus OH, USA
- Internal Medicine, The Ohio State University, Columbus OH, USA
- Radiology, The Ohio State University, Columbus OH, USA
| | - Rizwan Ahmad
- Biomedical Engineering, The Ohio State University, Columbus OH, USA
- Electrical and Computer Engineering, The Ohio State University, Columbus OH, USA
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus OH, USA
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