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Naumova A, Zhao XQ, Yuan C. MRI Quantification of Cardiac Structure and Function in Cardiomyopathy Patients. Methods Mol Biol 2024; 2735:17-26. [PMID: 38038841 DOI: 10.1007/978-1-0716-3527-8_2] [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] [Indexed: 12/02/2023]
Abstract
Cardiac Magnetic Resonance Imaging (CMRI) is a quantitative technique that enables non-invasive assessment of heart structure and contractile function as well as the mechanisms underlying cardiovascular disease. Here we provide step-by-step instructions and imaging protocols for conducting cardiac MRI exam on the patients with cardiomyopathies. Our imaging protocols are specific to the 3 Tesla magnetic field strength.
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Affiliation(s)
| | | | - Chun Yuan
- University of Washington, Seattle, WA, USA
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2
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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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Hufnagel S, Metzner S, Kerkering KM, Aigner CS, Kofler A, Schulz-Menger J, Schaeffter T, Kolbitsch C. 3D model-based super-resolution motion-corrected cardiac T1 mapping. Phys Med Biol 2022; 67. [PMID: 36265478 DOI: 10.1088/1361-6560/ac9c40] [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: 07/25/2022] [Accepted: 10/20/2022] [Indexed: 12/13/2022]
Abstract
Objective. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR).Approach. Due to signal-to-noise ratio limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6-8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6-8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5-2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects.Main results. Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%.Significance. The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3 × 1.3 × 1.5-2 mm3). The approach led to improvements in the visualization of small structures and precise T1 values.
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Affiliation(s)
- Simone Hufnagel
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Selma Metzner
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | | | - Andreas Kofler
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité Medical Faculty University Medicine, Berlin, Germany.,Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center (ECRC), Charité Humboldt University Berlin, DZHK partner site Berlin, Berlin, Germany.,Department of Cardiology and Nephrology, HELIOS Klinikum Berlin Buch, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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4
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Cao T, Wang N, Kwan AC, Lee HL, Mao X, Xie Y, Nguyen KL, Colbert CM, Han F, Han P, Han H, Christodoulou AG, Li D. Free-breathing, non-ECG, simultaneous myocardial T 1 , T 2 , T 2 *, and fat-fraction mapping with motion-resolved cardiovascular MR multitasking. Magn Reson Med 2022; 88:1748-1763. [PMID: 35713184 PMCID: PMC9339519 DOI: 10.1002/mrm.29351] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/18/2022] [Accepted: 05/17/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop a free-breathing, non-electrocardiogram technique for simultaneous myocardial T1 , T2 , T2 *, and fat-fraction (FF) mapping in a single scan. METHODS The MR Multitasking framework is adapted to quantify T1 , T2 , T2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high-dimensional image is modeled as a low-rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques. RESULTS In static and motion phantoms, T1 /T2 /T2 */FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1 , T2 , T2 *, and FF measurements that agreed with those from references. CONCLUSIONS The proposed free-breathing, non-electrocardiogram, motion-resolved Multitasking technique allows simultaneous quantification of myocardial T1 , T2 , T2 *, and FF in a single 2.5-min scan.
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Affiliation(s)
- Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Nan Wang
- Radiology Department, Stanford University, Stanford, California, USA
| | - Alan C. Kwan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Imaging and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Hsu-Lei Lee
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xianglun Mao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Kim-Lien Nguyen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
- David Geffen School of Medicine and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Caroline M. Colbert
- David Geffen School of Medicine and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
- Physics and Biology in Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Pei Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
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5
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Guo R, Chen Z, Amyar A, El-Rewaidy H, Assana S, Rodriguez J, Pierce P, Goddu B, Nezafat R. Improving accuracy of myocardial T 1 estimation in MyoMapNet. Magn Reson Med 2022; 88:2573-2582. [PMID: 35916305 DOI: 10.1002/mrm.29397] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To improve the accuracy and robustness of T1 estimation by MyoMapNet, a deep learning-based approach using 4 inversion-recovery T1 -weighted images for cardiac T1 mapping. METHODS MyoMapNet is a fully connected neural network for T1 estimation of an accelerated cardiac T1 mapping sequence, which collects 4 T1 -weighted images by a single Look-Locker inversion-recovery experiment (LL4). MyoMapNet was originally trained using in vivo data from the modified Look-Locker inversion recovery sequence, which resulted in significant bias and sensitivity to various confounders. This study sought to train MyoMapNet using signals generated from numerical simulations and phantom MR data under multiple simulated confounders. The trained model was then evaluated by phantom data scanned using new phantom vials that differed from those used for training. The performance of the new model was compared with modified Look-Locker inversion recovery sequence and saturation-recovery single-shot acquisition for measuring native and postcontrast T1 in 25 subjects. RESULTS In the phantom study, T1 values measured by LL4 with MyoMapNet were highly correlated with reference values from the spin-echo sequence. Furthermore, the estimated T1 had excellent robustness to changes in flip angle and off-resonance. Native and postcontrast myocardium T1 at 3 Tesla measured by saturation-recovery single-shot acquisition, modified Look-Locker inversion recovery sequence, and MyoMapNet were 1483 ± 46.6 ms and 791 ± 45.8 ms, 1169 ± 49.0 ms and 612 ± 36.0 ms, and 1443 ± 57.5 ms and 700 ± 57.5 ms, respectively. The corresponding extracellular volumes were 22.90% ± 3.20%, 28.88% ± 3.48%, and 30.65% ± 3.60%, respectively. CONCLUSION Training MyoMapNet with numerical simulations and phantom data will improve the estimation of myocardial T1 values and increase its robustness to confounders while also reducing the overall T1 mapping estimation time to only 4 heartbeats.
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Affiliation(s)
- Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Zhensen Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
| | - Amine Amyar
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Hossam El-Rewaidy
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Salah Assana
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Jennifer Rodriguez
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
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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: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [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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7
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Guo R, El-Rewaidy H, Assana S, Cai X, Amyar A, Chow K, Bi X, Yankama T, Cirillo J, Pierce P, Goddu B, Ngo L, Nezafat R. Accelerated cardiac T 1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T 1 estimation approach. J Cardiovasc Magn Reson 2022; 24:6. [PMID: 34986850 PMCID: PMC8734349 DOI: 10.1186/s12968-021-00834-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/30/2021] [Indexed: 11/24/2022] Open
Abstract
PURPOSE To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). METHOD We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI. RESULTS MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively. CONCLUSION A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.
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Affiliation(s)
- Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Hossam El-Rewaidy
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Salah Assana
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Xiaoying Cai
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
- Siemens Medical Solutions USA, Inc, Boston, MA, USA
| | - Amine Amyar
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Kelvin Chow
- Siemens Medical Solutions USA, Inc, Chicago, IL, USA
| | - Xiaoming Bi
- Siemens Medical Solutions USA, Inc, Chicago, IL, USA
| | - Tuyen Yankama
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Julia Cirillo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Long Ngo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, MA, 02215, Boston, USA.
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Zhu D, Ding H, Zviman MM, Halperin H, Schär M, Herzka DA. Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques. PLoS One 2021; 16:e0252777. [PMID: 34506496 PMCID: PMC8432823 DOI: 10.1371/journal.pone.0252777] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/23/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. METHODS Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2. RESULTS In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7-2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71-0.50) than volume-by-volume SENSE (0.68-0.30). CONCLUSIONS Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.
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Affiliation(s)
- Dan Zhu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Haiyan Ding
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - M. Muz Zviman
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Radiology, Perelman School of Medicine of The University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Henry Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Michael Schär
- Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Daniel A. Herzka
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Laboratory of Cardiovascular Intervention, National Heart Lung and Blood Institute, NIH, Bethesda, Maryland, United States of America
- * E-mail:
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9
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Single breath-hold saturation recovery 3D cardiac T1 mapping via compressed SENSE at 3T. MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2020; 33:865-876. [PMID: 32410103 PMCID: PMC7669807 DOI: 10.1007/s10334-020-00848-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 04/21/2020] [Accepted: 04/25/2020] [Indexed: 11/06/2022]
Abstract
Objectives To propose and validate a novel imaging sequence that uses a single breath-hold whole-heart 3D T1 saturation recovery compressed SENSE rapid acquisition (SACORA) at 3T. Methods The proposed sequence combines flexible saturation time sampling, compressed SENSE, and sharing of saturation pulses between two readouts acquired at different RR intervals. The sequence was compared with a 3D saturation recovery single-shot acquisition (SASHA) implementation with phantom and in vivo experiments (pre and post contrast; 7 pigs) and was validated against the reference inversion recovery spin echo (IR-SE) sequence in phantom experiments. Results Phantom experiments showed that the T1 maps acquired by 3D SACORA and 3D SASHA agree well with IR-SE. In vivo experiments showed that the pre-contrast and post-contrast T1 maps acquired by 3D SACORA are comparable to the corresponding 3D SASHA maps, despite the shorter acquisition time (15s vs. 188s, for a heart rate of 60 bpm). Mean septal pre-contrast T1 was 1453 ± 44 ms with 3D SACORA and 1460 ± 60 ms with 3D SASHA. Mean septal post-contrast T1 was 824 ± 66 ms and 824 ± 60 ms. Conclusion 3D SACORA acquires 3D T1 maps in 15 heart beats (heart rate, 60 bpm) at 3T. In addition to its short acquisition time, the sequence achieves good T1 estimation precision and accuracy. Electronic supplementary material The online version of this article (10.1007/s10334-020-00848-2) contains supplementary material, which is available to authorized users.
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10
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Guo R, Cai X, Kucukseymen S, Rodriguez J, Paskavitz A, Pierce P, Goddu B, Thompson RB, Nezafat R. Free-breathing simultaneous myocardial T 1 and T 2 mapping with whole left ventricle coverage. Magn Reson Med 2020; 85:1308-1321. [PMID: 33078443 DOI: 10.1002/mrm.28506] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 01/20/2023]
Abstract
PURPOSE To develop a free-breathing sequence, that is, Multislice Joint T1 -T2 , for simultaneous measurement of myocardial T1 and T2 for multiple slices to achieve whole left-ventricular coverage. METHODS Multislice Joint T1 -T2 adopts slice-interleaved acquisition to collect 10 single-shot electrocardiogram-triggered images for each slice prepared by saturation and T2 preparation to simultaneously estimate myocardial T1 and T2 and achieve whole left-ventricular coverage. Prospective slice-tracking using a respiratory navigator and retrospective image registration are used to reduce through-plane and in-plane motion, respectively. Multislice Joint T1 -T2 was validated through numerical simulations and phantom and in vivo experiments, and compared with saturation-recovery single-shot acquisition and T2 -prepared balanced Steady-State Free Precession (T2 -prep SSFP) sequences. RESULTS Phantom T1 and T2 from Multislice Joint T1 -T2 had good accuracy and precision, and were insensitive to heart rate. Multislice Joint T1 -T2 yielded T1 and T2 maps of nine left-ventricular slices in 1.4 minutes. The mean left-ventricular T1 difference between saturation-recovery single-shot acquisition and Multislice Joint T1 -T2 across healthy subjects and patients was 191 ms (1564 ± 60 ms versus 1373 ± 50 ms; P < .05) and 111 ms (1535 ± 49 ms vs 1423 ± 49 ms; P < .05), respectively. The mean difference in left-ventricular T2 between T2 -prep SSFP and Multislice Joint T1 -T2 across healthy subjects and patients was -6.3 ms (42.4 ± 1.4 ms vs 48.7 ± 2.5; P < .05) and -5.7 ms (41.6 ± 2.5 ms vs 47.3 ± 2.7; P < .05), respectively. CONCLUSION Multislice Joint T1 -T2 enables quantification of whole left-ventricular T1 and T2 during free breathing within a clinically feasible scan time of less than 2 minutes.
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Affiliation(s)
- Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Xiaoying Cai
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.,Siemens Medical Solutions USA, Inc., Boston, Massachusetts, USA
| | - Selcuk Kucukseymen
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Rodriguez
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Amanda Paskavitz
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Richard B Thompson
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
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11
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Guo R, Cai X, Kucukseymen S, Rodriguez J, Paskavitz A, Pierce P, Goddu B, Nezafat R. Free-breathing whole-heart multi-slice myocardial T 1 mapping in 2 minutes. Magn Reson Med 2020; 85:89-102. [PMID: 32662908 DOI: 10.1002/mrm.28402] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/13/2020] [Accepted: 06/08/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To develop and validate a saturation-delay-inversion recovery preparation, slice tracking and multi-slice based sequence for measuring whole-heart native T1 . METHOD The proposed free-breathing sequence performs T1 mapping of multiple left-ventricular slices by slice-interleaved acquisition to collect 10 electrocardiogram-triggered single-shot slice-selective images for each slice. A saturation-delay-inversion recovery pulse is used for T1 preparation. Prospective slice tracking by the diaphragm navigator and retrospective registration are used to reduce through-plane and in-plane motion, respectively. The proposed sequence was validated in both phantom and human subjects (12 healthy subjects and 15 patients who were referred for a clinical cardiac MR exam) and compared with saturation recovery single-shot acquisition (SASHA) and modified Look-Locker inversion recovery (MOLLI). RESULTS Phantom T1 measured by the proposed sequence had excellent agreement (R2 = 0.99) with the ground-truth T1 and was insensitive to heart rate. In both healthy subjects and patients, the proposed sequence yielded nine left-ventricular T1 maps per volume in less than 2 minutes (healthy volunteers: 1.8 ± 0.4 minutes; patients: 1.9 ± 0.2 minutes). The average T1 of whole left ventricle for all healthy subjects and patients were 1560 ± 61 and 1535 ± 49 ms by SASHA, 1208 ± 42 and 1233 ± 56 ms by MOLLI5(3)3, and 1397 ± 34 and 1433 ± 56 ms by the proposed sequence, respectively. The corresponding coefficient of variation of T1 were 6.2 ± 1.4% and 5.8 ± 1.6%, 5.3 ± 1.1% and 5.1 ± 0.8%, and 4.9 ± 0.8% and 4.5 ± 0.8%, respectively. CONCLUSION The proposed sequence enables quantification of whole heart T1 with good accuracy and precision in less than 2 minutes during free breathing.
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Affiliation(s)
- Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Xiaoying Cai
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.,Siemens Medical Solutions USA, Inc., Boston, MA, USA
| | - Selcuk Kucukseymen
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Jennifer Rodriguez
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Amanda Paskavitz
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
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12
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Milotta G, Bustin A, Jaubert O, Neji R, Prieto C, Botnar RM. 3D whole-heart isotropic-resolution motion-compensated joint T 1 /T 2 mapping and water/fat imaging. Magn Reson Med 2020; 84:3009-3026. [PMID: 32544278 DOI: 10.1002/mrm.28330] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 04/27/2020] [Accepted: 04/28/2020] [Indexed: 01/12/2023]
Abstract
PURPOSE To develop a free-breathing isotropic-resolution whole-heart joint T1 and T2 mapping sequence with Dixon-encoding that provides coregistered 3D T1 and T2 maps and complementary 3D anatomical water and fat images in a single ~9 min scan. METHODS Four interleaved dual-echo Dixon gradient echo volumes are acquired with a variable density Cartesian trajectory and different preparation pulses: 1) inversion recovery-preparation, 2) and 3) no preparations, and 4) T2 preparation. Image navigators are acquired to correct each echo for 2D translational respiratory motion; the 8 echoes are jointly reconstructed with a low-rank patch-based reconstruction. A water/fat separation algorithm is used to obtain water and fat images for each acquired volume. T1 and T2 maps are generated by matching the signal evolution of the water images to a simulated dictionary. Complementary bright-blood and fat volumes for anatomical visualization are obtained from the T2 -prepared dataset. The proposed sequence was tested in phantom experiments and 10 healthy subjects and compared to standard 2D MOLLI T1 mapping, 2D balance steady-state free precession T2 mapping, and 3D T2 -prepared Dixon coronary MR angiography. RESULTS High linear correlation was found between T1 and T2 quantification with the proposed approach and phantom spin echo measurements (y = 1.1 × -11.68, R2 = 0.98; and y = 0.85 × +5.7, R2 = 0.99). Mean myocardial values of T1 /T2 = 1116 ± 30.5 ms/45.1 ± 2.38 ms were measured in vivo. Biases of T1 /T2 = 101.8 ms/-0.77 ms were obtained compared to standard 2D techniques. CONCLUSION The proposed joint T1 /T2 sequence permitted the acquisition of motion-compensated isotropic-resolution 3D T1 and T2 maps and complementary coronary MR angiography and fat volumes, showing promising results in terms of T1 and T2 quantification and visualization of cardiac anatomy and pericardial fat.
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Affiliation(s)
- Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Aurelien Bustin
- 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
| | - 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
| | - 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
| | - 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
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13
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Han PK, Horng DE, Marin T, Petibon Y, Ouyang J, El Fakhri G, Ma C. Free-Breathing Three-Dimensional T 1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4008-4011. [PMID: 31946750 DOI: 10.1109/embc.2019.8856511] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mapping the longitudinal relaxation time constant (T1) of the myocardium using Magnetic Resonance Imaging (MRI) is an emerging technique for quantitative assessment of the morphology and viability of the myocardium. However, three-dimensional (3D) T1 mapping of the heart is challenging due to the high dimensionality of the signal and the presence of cardiac and respiratory motions. We propose a subspace-based method for free-breathing 3D T1 mapping of the heart without respiratory gating. The image function is represented as a high-order partially separable (PS) function to explore the inherent spatiotemporal correlations of the underlying signal. A special data acquisition scheme enabled by the high-order PS model is used for sparse sampling of the (k,t)-space, where complementary sparse datasets are acquired, each covering only a small portion of the (k,t)-space to characterize a single subspace (spatial or temporal). High-resolution dynamic MR images are reconstructed from the highly undersampled (k,t)-space using low-rank tensor and sparsity constraints. We demonstrate the feasibility of our proposed method using in vivo data obtained from healthy subjects on a 3T MR scanner. The proposed method can enable new clinical applications of T1 mapping in cardiac MR.
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14
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Manning WJ. Journal of Cardiovascular Magnetic Resonance: 2017/2018 in review. J Cardiovasc Magn Reson 2019; 21:79. [PMID: 31884956 PMCID: PMC6936125 DOI: 10.1186/s12968-019-0594-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/14/2022] Open
Abstract
There were 89 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2017, including 76 original research papers, 4 reviews, 5 technical notes, 1 guideline, and 3 corrections. The volume was down slightly from 2017 with a corresponding 15% decrease in manuscript submissions from 405 to 346 and thus reflects a slight increase in the acceptance rate from 25 to 26%. The decrease in submissions for the year followed the initiation of the increased author processing charge (APC) for Society for Cardiovascular Magnetic Resonance (SCMR) members for manuscripts submitted after June 30, 2018. The quality of the submissions continues to be high. The 2018 JCMR Impact Factor (which is published in June 2019) was slightly lower at 5.1 (vs. 5.46 for 2017; as published in June 2018. The 2018 impact factor means that on average, each JCMR published in 2016 and 2017 was cited 5.1 times in 2018. Our 5 year impact factor was 5.82.In accordance with Open-Access publishing guidelines of BMC, the JCMR articles are published on-line in a continuus fashion in the chronologic order of acceptance, with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful for the JCMR audience to annually summarize the publications into broad areas of interest or themes, so that readers can view areas of interest in a single article in relation to each other and contemporaneous JCMR publications. In this publication, the manuscripts are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought within the journal. In addition, as in the past two years, I have used this publication to also convey information regarding the editorial process and as a "State of our JCMR."This is the 12th year of JCMR as an open-access publication with BMC (formerly known as Biomed Central). The timing of the JCMR transition to the open access platform was "ahead of the curve" and a tribute to the vision of Dr. Matthias Friedrich, the SCMR Publications Committee Chair and Dr. Dudley Pennell, the JCMR editor-in-chief at the time. The open-access system has dramatically increased the reading and citation of JCMR publications and I hope that you, our authors, will continue to send your very best, high quality manuscripts to JCMR for consideration. It takes a village to run a journal and I thank our very dedicated Associate Editors, Guest Editors, Reviewers for their efforts to ensure that the review process occurs in a timely and responsible manner. These efforts have allowed the JCMR to continue as the premier journal of our field. This entire process would also not be possible without the dedication and efforts of our managing editor, Diana Gethers. Finally, I thank you for entrusting me with the editorship of the JCMR as I begin my 4th year as your editor-in-chief. It has been a tremendous experience for me and the opportunity to review manuscripts that reflect the best in our field remains a great joy and highlight of my week!
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Affiliation(s)
- Warren J Manning
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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15
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Qi H, Bustin A, Cruz G, Jaubert O, Chen H, Botnar RM, Prieto C. Free-running simultaneous myocardial T1/T2 mapping and cine imaging with 3D whole-heart coverage and isotropic spatial resolution. Magn Reson Imaging 2019; 63:159-169. [DOI: 10.1016/j.mri.2019.08.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 08/10/2019] [Accepted: 08/15/2019] [Indexed: 12/14/2022]
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16
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Qi H, Jaubert O, Bustin A, Cruz G, Chen H, Botnar R, Prieto C. Free-running 3D whole heart myocardial T 1 mapping with isotropic spatial resolution. Magn Reson Med 2019; 82:1331-1342. [PMID: 31099442 PMCID: PMC6851769 DOI: 10.1002/mrm.27811] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 04/15/2019] [Accepted: 04/19/2019] [Indexed: 01/14/2023]
Abstract
PURPOSE To develop a free-running (free-breathing, retrospective cardiac gating) 3D myocardial T1 mapping with isotropic spatial resolution. METHODS The free-running sequence is inversion recovery (IR)-prepared followed by continuous 3D golden angle radial data acquisition. 1D respiratory motion signal is extracted from the k-space center of all spokes and used to bin the k-space data into different respiratory states, enabling estimation and correction of 3D translational respiratory motion, whereas cardiac motion is recorded using electrocardiography and synchronized with data acquisition. 3D translational respiratory motion compensated T1 maps at diastole and systole were generated with 1.5 mm isotropic spatial resolution with low-rank inversion and high-dimensionality patch-based undersampled reconstruction. The technique was validated against conventional methods in phantom and 9 healthy subjects. RESULTS Phantom results demonstrated good agreement (R2 = 0.99) of T1 estimation with reference method. Homogeneous systolic and diastolic 3D T1 maps were reconstructed from the proposed technique. Diastolic septal T1 estimated with the proposed method (1140 ± 36 ms) was comparable to the saturation recovery single-shot acquisition (SASHA) sequence (1153 ± 49 ms), but was higher than the modified Look-Locker inversion recovery (MOLLI) sequence (1037 ± 33 ms). Precision of the proposed method (42 ± 8 ms) was comparable to MOLLI (41 ± 7 ms) and improved with respect to SASHA (87 ± 19 ms). CONCLUSIONS The proposed free-running whole heart T1 mapping method allows for reconstruction of isotropic resolution 3D T1 maps at different cardiac phases, serving as a promising tool for whole heart myocardial tissue characterization.
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Affiliation(s)
- Haikun Qi
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical EngineeringTsinghua UniversityBeijingChina
| | - René Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
- Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
- Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile
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17
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Huang L, Neji R, Nazir MS, Whitaker J, Duong P, Reid F, Bosio F, Chiribiri A, Razavi R, Roujol S. FASt single-breathhold 2D multislice myocardial T 1 mapping (FAST1) at 1.5T for full left ventricular coverage in three breathholds. J Magn Reson Imaging 2019; 51:492-504. [PMID: 31342614 PMCID: PMC6954880 DOI: 10.1002/jmri.26869] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 07/02/2019] [Indexed: 12/28/2022] Open
Abstract
Background Conventional myocardial T1 mapping techniques such as modified Look–Locker inversion recovery (MOLLI) generate one T1 map per breathhold. T1 mapping with full left ventricular coverage may be desirable when spatial T1 variations are expected. This would require multiple breathholds, increasing patient discomfort and prolonging scan time. Purpose To develop and characterize a novel FASt single‐breathhold 2D multislice myocardial T1 mapping (FAST1) technique for full left ventricular coverage. Study Type Prospective. Population/Phantom Numerical simulation, agarose/NiCl2 phantom, 9 healthy volunteers, and 17 patients. Field Strength/Sequence 1.5T/FAST1. Assessment Two FAST1 approaches, FAST1‐BS and FAST1‐IR, were characterized and compared with standard 5‐(3)‐3 MOLLI in terms of accuracy, precision/spatial variability, and repeatability. Statistical Tests Kruskal‐Wallis, Wilcoxon signed rank tests, intraclass correlation coefficient analysis, analysis of variance, Student's t‐tests, Pearson correlation analysis, and Bland–Altman analysis. Results In simulation/phantom, FAST1‐BS, FAST1‐IR, and MOLLI had an accuracy (expressed as T1 error) of 0.2%/4%, 6%/9%, and 4%/7%, respectively, while FAST1‐BS and FAST1‐IR had a precision penalty of 1.7/1.5 and 1.5/1.4 in comparison with MOLLI, respectively. In healthy volunteers, FAST1‐BS/FAST1‐IR/MOLLI led to different native myocardial T1 times (1016 ± 27 msec/952 ±22 msec/987 ± 23 msec, P < 0.0001) and spatial variability (66 ± 10 msec/57 ± 8 msec/46 ± 7 msec, P < 0.001). There were no statistically significant differences between all techniques for T1 repeatability (P = 0.18). In vivo native and postcontrast myocardial T1 times in both healthy volunteers and patients using FAST1‐BS/FAST1‐IR were highly correlated with MOLLI (Pearson correlation coefficient ≥0.93). Data Conclusion FAST1 enables myocardial T1 mapping with full left ventricular coverage in three separated breathholds. In comparison with MOLLI, FAST1 yield a 5‐fold increase of spatial coverage, limited penalty of T1 precision/spatial variability, no significant difference of T1 repeatability, and highly correlated T1 times. FAST1‐IR provides improved T1 precision/spatial variability but reduced accuracy when compared with FAST1‐BS. Level of Evidence: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:492–504.
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Affiliation(s)
- Li Huang
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Radhouene Neji
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Muhummad Sohaib Nazir
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - John Whitaker
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Phuoc Duong
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Fiona Reid
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Filippo Bosio
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Amedeo Chiribiri
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Reza Razavi
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Sébastien Roujol
- The School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
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18
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Aherne E, Chow K, Carr J. Cardiac T 1 mapping: Techniques and applications. J Magn Reson Imaging 2019; 51:1336-1356. [PMID: 31334899 DOI: 10.1002/jmri.26866] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 06/27/2019] [Accepted: 06/27/2019] [Indexed: 12/18/2022] Open
Abstract
A key advantage of cardiac magnetic resonance (CMR) imaging over other cardiac imaging modalities is the ability to perform detailed tissue characterization. CMR techniques continue to evolve, with advanced imaging sequences being developed to provide a reproducible, quantitative method of tissue interrogation. The T1 mapping technique, a pixel-by-pixel method of quantifying T1 relaxation time of soft tissues, has been shown to be promising for characterization of diseased myocardium in a wide variety of cardiomyopathies. In this review, we describe the basic principles and common techniques for T1 mapping and its use for native T1 , postcontrast T1 , and extracellular volume mapping. We will review a wide range of clinical applications of the technique that can be used for identification and quantification of myocardial edema, fibrosis, and infiltrative diseases with illustrative clinical examples. In addition, we will explore the current limitations of the technique and describe some areas of ongoing development. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1336-1356.
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Affiliation(s)
- Emily Aherne
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Kelvin Chow
- Department of Radiology, Northwestern University, Chicago, Illinois, USA.,Cardiovascular MR R&D, Siemens Medical Solutions USA, Inc., Chicago, Illinois, USA
| | - James Carr
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
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