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Si D, Crabb MG, Kunze KP, Littlewood SJ, Prieto C, Botnar RM. Free-breathing 3D whole-heart joint T 1/T 2 mapping and water/fat imaging at 0.55 T. Magn Reson Med 2024; 92:1511-1524. [PMID: 38872384 DOI: 10.1002/mrm.30139] [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: 01/24/2024] [Revised: 03/20/2024] [Accepted: 04/16/2024] [Indexed: 06/15/2024]
Abstract
PURPOSE To develop and validate a highly efficient motion compensated free-breathing isotropic resolution 3D whole-heart joint T1/T2 mapping sequence with anatomical water/fat imaging at 0.55 T. METHODS The proposed sequence takes advantage of shorter T1 at 0.55 T to acquire three interleaved water/fat volumes with inversion-recovery preparation, no preparation, and T2 preparation, respectively. Image navigators were used to facilitate nonrigid motion-compensated image reconstruction. T1 and T2 maps were jointly calculated by a dictionary matching method. Validations were performed with simulation, phantom, and in vivo experiments on 10 healthy volunteers and 1 patient. The performance of the proposed sequence was compared with conventional 2D mapping sequences including modified Look-Locker inversion recovery and T2-prepared balanced steady-SSFP sequence. RESULTS The proposed sequence has a good T1 and T2 encoding sensitivity in simulation, and excellent agreement with spin-echo reference T1 and T2 values was observed in a standardized T1/T2 phantom (R2 = 0.99). In vivo experiments provided good-quality co-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in a short scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean and SD measured by the proposed sequence were both comparable with those of modified Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3 vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by the proposed sequence were both slightly lower than those of T2-prepared balanced SSFP (53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). Myocardial T1 and T2 in the patient measured by the proposed sequence were in good agreement with conventional 2D sequences and late gadolinium enhancement. CONCLUSION The proposed sequence simultaneously acquires 3D whole-heart T1 and T2 mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan. Further investigation in patients with cardiovascular disease is now warranted.
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Affiliation(s)
- Dongyue Si
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michael G Crabb
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Karl P Kunze
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Simon J Littlewood
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
- Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
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Muslu Y, Tamada D, Roberts NT, Cashen TA, Mandava S, Kecskemeti SR, Hernando D, Reeder SB. Free-breathing, fat-corrected T 1 mapping of the liver with stack-of-stars MRI, and joint estimation of T 1, PDFF, R 2 * , and B 1 + . Magn Reson Med 2024. [PMID: 38923009 DOI: 10.1002/mrm.30182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 05/03/2024] [Accepted: 05/16/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat andB 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition. THEORY AND METHODS Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T. RESULTS The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps. CONCLUSION The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, andB 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.
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Affiliation(s)
- Yavuz Muslu
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daiki Tamada
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | | | | | | | - Diego Hernando
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Hufnagel S, Schuenke P, Schulz-Menger J, Schaeffter T, Kolbitsch C. 3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks. Phys Med Biol 2024; 69:085027. [PMID: 38479021 DOI: 10.1088/1361-6560/ad33b6] [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: 11/06/2023] [Accepted: 03/13/2024] [Indexed: 04/10/2024]
Abstract
Objective. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks.Approach. Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4-8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic cardiac T1 maps. However, they were limited to the ventricles. The proposed approach acquires rotated stacks in long-axis orientation with high in-plane resolution but low through-plane resolution. This results in radially overlapping stacks from which high-resolution T1 maps of the whole heart are reconstructed using a k-space-based SRR framework considering the complete acquisition model. Cardiac and residual respiratory motion between different breath holds is estimated and incorporated into the reconstruction. The proposed approach was evaluated in simulations and phantom experiments and successfully applied to ten healthy subjects.Main results. 3D T1 maps of the whole heart were obtained in the same acquisition time as previous methods covering only the ventricles. T1 measurements were possible even for small structures, such as the atrial wall. The proposed approach provided accurate (P> 0.4;R2> 0.99) and precise T1 values (SD of 64.32 ± 22.77 ms in the proposed approach, 44.73 ± 31.9 ms in the reference). The edge sharpness of the T1 maps was increased by 6.20% and 4.73% in simulation and phantom experiments, respectively. Contrast-to-noise ratios between the septum and blood pool increased by 14.50% inin vivomeasurements with a k-space compared to an image-space-based SRR.Significance. The proposed approach provided whole-heart high-resolution 1.3 mm isotropic T1 maps in an overall acquisition time of approximately three minutes. Small structures, such as the atrial and right ventricular walls, could be visualized in the T1 maps.
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Affiliation(s)
- Simone Hufnagel
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Patrick Schuenke
- 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
- Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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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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Wang X, Rosenzweig S, Roeloffs V, Blumenthal M, Scholand N, Tan Z, Holme HCM, Unterberg-Buchwald C, Hinkel R, Uecker M. Free-breathing myocardial T 1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction. Magn Reson Med 2023; 89:1368-1384. [PMID: 36404631 PMCID: PMC9892313 DOI: 10.1002/mrm.29521] [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: 11/17/2021] [Revised: 09/22/2022] [Accepted: 10/20/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To develop a free-breathing myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction. METHODS Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incompleteT 1 $$ {\mathrm{T}}_1 $$ recovery into account for an accurateT 1 $$ {\mathrm{T}}_1 $$ calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction. RESULTS In comparison to an IR spin-echo reference, phantom results confirm goodT 1 $$ {\mathrm{T}}_1 $$ accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improvesT 1 $$ {\mathrm{T}}_1 $$ precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardialT 1 $$ {\mathrm{T}}_1 $$ maps can be obtained within 2 min with good precision and repeatability. CONCLUSION Motion-resolved myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.
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Affiliation(s)
- Xiaoqing Wang
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Sebastian Rosenzweig
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Volkert Roeloffs
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
| | - Moritz Blumenthal
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
| | - Nick Scholand
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Zhengguo Tan
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | | | - Christina Unterberg-Buchwald
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Rabea Hinkel
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
- Laboratory Animal Science Unit, Leibniz Institute for Primate Research, Deutsches Primatenzentrum GmbH, Göttingen, Germany
- Institute for Animal Hygiene, Animal Welfare and Farm Animal Behavior, University of Veterinary Medicine, Hannover, Germany
| | - Martin Uecker
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
- Cluster of “Excellence Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Germany
- BioTechMed-Graz, Graz, Austria
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Wang Y, Liu X, Wang J, Wang Y, Qi H, Kong X, Liu D, Liu J, Zheng H, Xiong F, Zhang L, Fu X, Zhang X, Guo R, Qiao H, Chen Z, Si D, Chen H. Simultaneous T1, T2, and T2* Mapping of Carotid Plaque: The SIMPLE* Technique. Radiology 2023; 307:e222061. [PMID: 36853181 DOI: 10.1148/radiol.222061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Background Quantitative T1, T2, and T2* measurements of carotid atherosclerotic plaque are important in evaluating plaque vulnerability and monitoring its progression. Purpose To develop a sequence to simultaneously quantify T1, T2, and T2* of carotid plaque. Materials and Methods The simultaneous T1, T2, and T2* mapping of carotid plaque (SIMPLE*) sequence is composed of three modules with different T2 preparation pulses, inversion-recovery pulses, and acquisition schemas. Single-echo data were used for T1 and T2 quantification, while the multiecho (ME) data were used for T2* quantification. The quantitative accuracy of SIMPLE* was tested in a phantom study by comparing its measurements with those of reference standard sequences. In vivo feasibility of the technique was prospectively evaluated between November 2020 and February 2022 in healthy volunteers and participants with carotid atherosclerotic plaque. The Pearson or Spearman correlation test, Student t test, and Wilcoxon rank-sum test were used. Results T1, T2, and T2* estimated with SIMPLE* strongly correlated with inversion-recovery spin-echo (SE) (correlation coefficient [r] = 0.99), ME-SE (r = 0.99), and ME gradient-echo (r = 0.99) sequences in the phantom study. In five healthy volunteers (mean age, 25 years ± 3 [SD]; three women), measurements were similar between SIMPLE* and modified Look-Locker inversion recovery, or MOLLI (1151 msec ± 71 vs 1098 msec ± 64; P = .14), ME turbo SE (31 msec ± 1 vs 31 msec ± 1; P = .32), and ME turbo field echo (24 msec ± 2 vs 25 msec ± 2; P = .19). In 18 participants with carotid plaque (mean age, 65 years ± 9; 16 men), quantitative T1, T2, and T2* of plaque components were consistent with their signal characteristics on multicontrast images. Conclusion A quantitative technique for simultaneous T1, T2, and T2* mapping of carotid plaque with 100-mm3 coverage and 0.8-mm3 resolution was developed using the proposed SIMPLE* sequence and demonstrated high accuracy and in vivo feasibility. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Yajie Wang
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Xiaoming Liu
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Jing Wang
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Yishi Wang
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Haikun Qi
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Xiangchuang Kong
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Dingxi Liu
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Jia Liu
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Hanpei Zheng
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Fu Xiong
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Lan Zhang
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Xiaona Fu
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Xinli Zhang
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Rui Guo
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Huiyu Qiao
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Zhensen Chen
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Dongyue Si
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
| | - Huijun Chen
- From the Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China 100084 (Yajie Wang, H. Qiao, D.S., H.C.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (X.L., J.W., X.K., D.L., J.L., H.Z., F.X., L.Z., X.F., X.Z.); Philips Healthcare, Beijing, China (Yishi Wang); School of Biomedical Engineering, ShanghaiTech University, Shanghai, China (H. Qi); School of Medical Technology, Beijing Institution of Technology, Beijing, China (R.G.); and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Z.C.)
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7
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Wang Y, Qi H, Wang Y, Xiao M, Xiang C, Dong J, Chen H. Free-breathing simultaneous water-fat separation and T1 mapping of the whole liver (SWALI) with isotropic resolution using 3D golden-angle radial trajectory. Quant Imaging Med Surg 2023; 13:912-923. [PMID: 36819282 PMCID: PMC9929423 DOI: 10.21037/qims-22-748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023]
Abstract
Background Conventional liver T1 mapping techniques are typically performed under breath-holding conditions; they have limited slice coverage and often rely on multiple acquisitions. Furthermore, liver fat affects the accuracy of T1 quantification. Therefore, we aim to propose a free-breathing technique for simultaneous water-fat separation and T1 mapping of the whole liver (SWALI) in a single scan. Methods The proposed SWALI sequence included an inversion recovery (IR) preparation pulse followed by a series of multiecho three-dimensional (3D) golden-angle radial acquisitions. For each echo time (TE), a series of images containing a mix of water and fat were reconstructed using a sliding window method. For each inversion time (TI), water and fat were separated, and then water and fat T1 estimation was conducted. The fat fraction (FF) was calculated based on the last TI image. The FF and water T1 quantification accuracy were compared with the gold standard sequences in the phantom. The in vivo feasibility was tested in 9 healthy volunteers, 2 patients with fatty liver, and 3 patients with hepatocellular carcinoma (HCC). The reproducibility was evaluated in the patients with fatty liver and in the healthy volunteers. Results The mean FF and the mean water T1 values obtained by the SWALI sequence showed good agreements with chemical shift-encoded magnetic resonance imaging (CSE-MRI; r=0.998; P<0.001) and fat-suppressed (FS) IR-spin echo (SE; r=0.997; P<0.001) in the phantom. For the patients with fatty liver and the healthy volunteers, the SWALI sequence showed no significant difference with CSE-MRI in FF quantification (P=0.53). In T1 quantification, comparable T1 values were obtained with the SWALI sequence and modified Look-Locker inversion recovery (MOLLI; P=0.10) in healthy volunteers, while the water T1 estimated by the SWALI sequence was significantly lower than the water-fat compound T1 estimated by MOLLI (P<0.001) in patients with fatty liver. In the reproducibility study, the intraclass correlation coefficients (ICCs) for the estimated FF and water T1 were 0.997 and 0.943, respectively. Water T1 of the patients with HCC calculated using the SWALI sequence showed a significant reduction after the contrast administration (P<0.001). Conclusions Free-breathing water-fat separation and T1 mapping of the whole liver with 2.5 mm isotropic spatial resolution were achieved simultaneously using the SWALI sequence in a 5-min scan.
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Affiliation(s)
- Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | | | - Ming Xiao
- Department of Hepatobiliary Surgery, The Second Hospital of Shandong University, Jinan, China;,Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Canhong Xiang
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Jiahong Dong
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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8
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Phair A, Cruz G, Qi H, Botnar RM, Prieto C. Free-running 3D whole-heart T 1 and T 2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction. Magn Reson Med 2023; 89:217-232. [PMID: 36198014 PMCID: PMC9828568 DOI: 10.1002/mrm.29449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/14/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images, enabling a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3-min scan. METHODS Data were acquired using a free-running 3D golden-angle radial readout interleaved with inversion recovery and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> -preparation pulses. After correction for translational respiratory motion, non-rigid cardiac-motion-corrected reconstruction with dictionary-based low-rank compression and patch-based regularization enabled 3D whole-heart <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> mapping at any given cardiac phase as well as whole-heart cardiac cine imaging. The framework was validated and compared with established methods in 11 healthy subjects. RESULTS Good quality 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images were reconstructed for all subjects. Septal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> values using the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1200</mml:mn> <mml:mo>±</mml:mo> <mml:mn>50</mml:mn></mml:mrow> <mml:annotation>$$ 1200\pm 50 $$</mml:annotation></mml:semantics> </mml:math> ms) were higher than those from a 2D MOLLI sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1063</mml:mn> <mml:mo>±</mml:mo> <mml:mn>33</mml:mn></mml:mrow> <mml:annotation>$$ 1063\pm 33 $$</mml:annotation></mml:semantics> </mml:math> ms), which is known to underestimate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> , while <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> values from the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 4 $$</mml:annotation></mml:semantics> </mml:math> ms) were in good agreement with those from a 2D GraSE sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 2 $$</mml:annotation></mml:semantics> </mml:math> ms). CONCLUSION The proposed technique provides 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images with isotropic spatial resolution in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3.3-min scan.
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Affiliation(s)
- Andrew Phair
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Haikun Qi
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - René M. Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Instituto de Ingeniería Biológica y MédicaPontificia Universidad Católica de ChileSantiagoChile,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
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9
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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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10
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Ho N, Kim YC. Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model. Tomography 2022; 8:2749-2760. [PMID: 36412688 PMCID: PMC9680453 DOI: 10.3390/tomography8060229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/16/2022] Open
Abstract
Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convolutional neural network (CNN) and recurrent neural network (RNN) that takes a series of short axis slices as input and predicts a series of slice levels as output. Each slice image was labeled as one of the following five classes: out-of-apical, apical, mid, basal, and out-of-basal levels. A variety of multi-class classification models were evaluated. When compared with the CNN-alone models, the cascaded CNN-RNN models resulted in higher mean F1-score and accuracy. In our implementation and testing of four different baseline networks with different combinations of RNN modules, MobileNet as the feature extractor cascaded with a two-layer long short-term memory (LSTM) network produced the highest scores in four of the seven evaluation metrics, i.e., five F1-scores, area under the curve (AUC), and accuracy. Our study indicates that the cascaded CNN-RNN models are superior to the CNN-alone models for the classification of short axis slice levels in cardiac cine MR images.
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Affiliation(s)
- Namgyu Ho
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Seoul 02455, Republic of Korea
- Department of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea
| | - Yoon-Chul Kim
- Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea
- Correspondence:
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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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12
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Guo R, Si D, Chen Z, Dai E, Chen S, Herzka DA, Luo J, Ding H. SAturation-recovery and Variable-flip-Angle-based three-dimensional free-breathing cardiovascular magnetic resonance T 1 mapping at 3 T. NMR IN BIOMEDICINE 2022; 35:e4755. [PMID: 35485432 DOI: 10.1002/nbm.4755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 06/14/2023]
Abstract
The purpose of the current study was to develop and validate a three-dimensional (3D) free-breathing cardiac T1 -mapping sequence using SAturation-recovery and Variable-flip-Angle (SAVA). SAVA sequentially acquires multiple electrocardiogram-triggered volumes using a multishot spoiled gradient-echo sequence. The first volume samples the equilibrium signal of the longitudinal magnetization, where a flip angle of 2° is used to reduce the time for the magnetization to return to equilibrium. The succeeding three volumes are saturation prepared with variable delays, and are acquired using a 15° flip angle to maintain the signal-to-noise ratio. A diaphragmatic navigator is used to compensate the respiratory motion. T1 is calculated using a saturation-recovery model that accounts for the flip angle. We validated SAVA by simulations, phantom, and human subject experiments at 3 T. SAVA was compared with modified Look-Locker inversion recovery (MOLLI) and saturation-recovery single-shot acquisition (SASHA) in vivo. In phantoms, T1 by SAVA had good agreement with the reference (R2 = 0.99). In vivo 3D T1 mapping by SAVA could achieve an imaging resolution of 1.25 × 1.25 × 8 mm3 . Both global and septal T1 values by SAVA (1347 ± 37 and 1332 ± 42 ms) were in between those by SASHA (1612 ± 63 and 1618 ± 51 ms) and MOLLI (1143 ± 59 and 1188 ± 65 ms). According to the standard deviation (SD) and coefficient of variation (CV), T1 precision measured by SAVA (SD: 99 ± 14 and 60 ± 8 ms; CV: 7.4% ± 0.9% and 4.5% ± 0.6%) was comparable with MOLLI (SD: 99 ± 25 and 46 ± 12 ms; CV: 8.8% ± 2.5% and 3.9% ± 1.1%) and superior to SASHA (SD: 222 ± 89 and 132 ± 33 ms; CV: 13.8% ± 5.5% and 8.1% ± 2.0%). It was concluded that the proposed free-breathing SAVA sequence enables more efficient 3D whole-heart T1 estimation with good accuracy and precision.
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Affiliation(s)
- Rui Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Dongyue Si
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Zhensen Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Shuo Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Daniel A Herzka
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jianwen Luo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Haiyan Ding
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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13
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Zhou R, Wang J, Weller DS, Yang Y, Mugler JP, Salerno M. Free-breathing self-gated continuous-IR spiral T1 mapping: Comparison of dual flip-angle and Bloch-Siegert B1-corrected techniques. Magn Reson Med 2022; 88:1068-1080. [PMID: 35481596 PMCID: PMC9325422 DOI: 10.1002/mrm.29269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 11/12/2022]
Abstract
Purpose To develop a B1‐corrrected single flip‐angle continuous acquisition strategy with free‐breathing and cardiac self‐gating for spiral T1 mapping, and compare it to a previous dual flip‐angle technique. Methods Data were continuously acquired using a spiral‐out trajectory, rotated by the golden angle in time. During the first 2 s, off‐resonance Fermi RF pulses were applied to generate a Bloch‐Siegert shift B1 map, and the subsequent data were acquired with an inversion RF pulse applied every 4 s to create a T1* map. The final T1 map was generated from the B1 and the T1* maps by using a look‐up table that accounted for slice profile effects, yielding more accurate T1 values. T1 values were compared to those from inversion recovery (IR) spin echo (phantom only), MOLLI, SAturation‐recovery single‐SHot Acquisition (SASHA), and previously proposed dual flip‐angle results. This strategy was evaluated in a phantom and 25 human subjects. Results The proposed technique showed good agreement with IR spin‐echo results in the phantom experiment. For in‐vivo studies, the proposed technique and the previously proposed dual flip‐angle method were more similar to SASHA results than to MOLLI results. Conclusions B1‐corrected single flip‐angle T1 mapping successfully acquired B1 and T1 maps in a free‐breathing, continuous‐IR spiral acquisition, providing a method with improved accuracy to measure T1 using a continuous Look‐Locker acquisition, as compared to the previously proposed dual excitation flip‐angle technique.
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Affiliation(s)
- Ruixi Zhou
- Department of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.,Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Junyu Wang
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, USA
| | | | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John P Mugler
- Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Michael Salerno
- Department of Medicine, Cardiovascular Medicine and Department of Radiology, Cardiovascular Imaging, Stanford University, Palo Alto, California, USA.,Department of Medicine, Cardiology Division, Radiology and Medical Imaging, and Biomedical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA
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14
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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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15
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Lima da Cruz GJ, Velasco C, Lavin B, Jaubert O, Botnar RM, Prieto C. Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting. Magn Reson Med 2022; 87:2757-2774. [PMID: 35081260 PMCID: PMC9306903 DOI: 10.1002/mrm.29171] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/06/2021] [Accepted: 01/05/2022] [Indexed: 12/11/2022]
Abstract
Purpose Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath‐hold scan. Methods Simultaneous, co‐registered, multi‐parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath‐hold scan. T2* quantification is achieved with an eight‐echo readout that requires a long cardiac acquisition window. A novel low‐rank motion‐corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques. Results The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left‐ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2‐GraSE (42.8 vs 50.6 ms), lower T2* values than eight‐echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six‐echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters. Conclusion The proposed cardiac MRF approach enables simultaneous, co‐registered mapping of T1, T2, T2*, and FF in a single breath‐hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.
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Affiliation(s)
- Gastao José Lima da Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University, Madrid, Spain
| | - Olivier Jaubert
- 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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16
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Serry FM, Ma S, Mao X, Han F, Xie Y, Han H, Li D, Christodoulou AG. Dual flip-angle IR-FLASH with spin history mapping for B1+ corrected T1 mapping: Application to T1 cardiovascular magnetic resonance multitasking. Magn Reson Med 2021; 86:3182-3191. [PMID: 34309072 PMCID: PMC8568626 DOI: 10.1002/mrm.28935] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 06/07/2021] [Accepted: 07/01/2021] [Indexed: 12/27/2022]
Abstract
PURPOSE To develop a single-scan method for B 1 + -corrected T1 mapping and apply it for free-breathing (FB) cardiac MR multitasking without electrocardiogram (ECG) triggering. METHODS One dual flip-angle (2FA) inversion recovery (IR)-FLASH scan provides two observations of T 1 ∗ (apparent T1 ) corresponding to two distinct combinations of the nominal FA α and B 1 + . Spatiotemporally coregistered T1 and B 1 + spin history maps are obtained by fitting the 2FA signal model. T1 estimate accuracy and repeatability for single flip-angle (1FA) and 2FA IR-FLASH sequence MR multitasking were evaluated at 3T. A T1 phantom was first imaged on the scanner table, then on two human subjects' thoraxes in both breath-hold (BH) and FB conditions. IR-turbo spin echo (IR-TSE) static phantom T1 measurements served as reference. In 10 healthy subjects, myocardial T1 was evaluated with ECG-free, FB multitasking sequences alongside ECG-triggered BH MOLLI. RESULTS For phantom-on-table T1 estimates, 2FA agreed better with IR-TSE (intraclass correlation coefficient [ICC] = 0.996, mean error ± SD = -1.6% ± 1.9%) than did 1FA (ICC = 0.922; mean error ± SD = -4.3% ± 12%). For phantom-on-thorax, 2FA was more repeatable and robust to respiration than 1FA (coefficient of variation [CoV] = 1.2% 2FA, = 11.3% 1FA). In vivo, in intrasession T1 repeatability, 2FA (septal CoV = 2.4%, six-segment CoV = 4.4%) outperformed 1FA (septal CoV = 3.1%, six-segment CoV = 5.5%). In six-segment T1 homogeneity, 2FA (CoV = 7.9%) also outperformed 1FA (CoV = 11.1%). CONCLUSION The 2FA IR-FLASH improves T1 estimate accuracy and repeatability over 1FA IR-FLASH, and enables single-scan B 1 + -corrected T1 mapping without BHs or ECG when used with MR multitasking.
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Affiliation(s)
- Fardad Michael Serry
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xianglun Mao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, CA, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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17
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Weingärtner S, Desmond KL, Obuchowski NA, Baessler B, Zhang Y, Biondetti E, Ma D, Golay X, Boss MA, Gunter JL, Keenan KE, Hernando D. Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group. Magn Reson Med 2021; 87:1184-1206. [PMID: 34825741 DOI: 10.1002/mrm.29084] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/20/2021] [Accepted: 10/27/2021] [Indexed: 12/26/2022]
Abstract
On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T1 . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.
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Affiliation(s)
- Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Kimberly L Desmond
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Yuxin Zhang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, D'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xavier Golay
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.,Gold Standard Phantoms Limited, Rochester, United Kingdom
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, Pennsylvania, USA
| | | | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Diego Hernando
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
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18
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Han PK, Marin T, Djebra Y, Landes V, Zhuo Y, El Fakhri G, Ma C. Free-breathing 3D cardiac T 1 mapping with transmit B 1 correction at 3T. Magn Reson Med 2021; 87:1832-1845. [PMID: 34812547 DOI: 10.1002/mrm.29097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/12/2021] [Accepted: 11/05/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a cardiac T1 mapping method for free-breathing 3D T1 mapping of the whole heart at 3 T with transmit B1 ( B 1 + ) correction. METHODS A free-breathing, electrocardiogram-gated inversion-recovery sequence with spoiled gradient-echo readout was developed and optimized for cardiac T1 mapping at 3 T. High-frame-rate dynamic images were reconstructed from sparse (k,t)-space data acquired along a stack-of-stars trajectory using a subspace-based method for accelerated imaging. Joint T1 and flip-angle estimation was performed in T1 mapping to improve its robustness to B 1 + inhomogeneity. Subject-specific timing of data acquisition was used in the estimation to account for natural heart-rate variations during the imaging experiment. RESULTS Simulations showed that accuracy and precision of T1 mapping can be improved with joint T1 and flip-angle estimation and optimized electrocardiogram-gated spoiled gradient echo-based inversion-recovery acquisition scheme. The phantom study showed good agreement between the T1 maps from the proposed method and the reference method. Three-dimensional cardiac T1 maps (40 slices) were obtained at a 1.9-mm in-plane and 4.5-mm through-plane spatial resolution from healthy subjects (n = 6) with an average imaging time of 14.2 ± 1.6 minutes (heartbeat rate: 64.2 ± 7.1 bpm), showing myocardial T1 values comparable to those obtained from modified Look-Locker inversion recovery. The proposed method generated B 1 + maps with spatially smooth variation showing 21%-32% and 11%-15% variations across the septal-lateral and inferior-anterior regions of the myocardium in the left ventricle. CONCLUSION The proposed method allows free-breathing 3D T1 mapping of the whole heart with transmit B1 correction in a practical imaging time.
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Affiliation(s)
- Paul Kyu Han
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Thibault Marin
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Yanis Djebra
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.,LTCI, Télécom Paris, Institut Polytechnique de Paris, France
| | | | - Yue Zhuo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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19
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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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Hoppe E, Wetzl J, Yoon SS, Bacher M, Roser P, Stimpel B, Preuhs A, Maier A. Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2105-2117. [PMID: 33848244 DOI: 10.1109/tmi.2021.3073091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, time-continous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of > 98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.
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21
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Feng L, Liu F, Soultanidis G, Liu C, Benkert T, Block KT, Fayad ZA, Yang Y. Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping. Magn Reson Med 2021; 86:97-114. [PMID: 33580909 PMCID: PMC8197608 DOI: 10.1002/mrm.28679] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping. METHODS An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver). RESULTS ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers. CONCLUSION This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chenyu Liu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Benkert
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Kai Tobias Block
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, USA
| | - Zahi A. Fayad
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Zhou R, Weller DS, Yang Y, Wang J, Jeelani H, Mugler JP, Salerno M. Dual-excitation flip-angle simultaneous cine and T 1 mapping using spiral acquisition with respiratory and cardiac self-gating. Magn Reson Med 2021; 86:82-96. [PMID: 33590591 PMCID: PMC8849625 DOI: 10.1002/mrm.28675] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/18/2020] [Accepted: 12/19/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To develop a free-breathing cardiac self-gated technique that provides cine images and B1+ slice profile-corrected T1 maps from a single acquisition. METHODS Without breath-holding or electrocardiogram gating, data were acquired continuously on a 3T scanner using a golden-angle gradient-echo spiral pulse sequence, with an inversion RF pulse applied every 4 seconds. Flip angles of 3° and 15° were used for readouts after the first four and second four inversions. Self-gating cardiac triggers were extracted from heart image navigators, and respiratory motion was corrected by rigid registration on each heartbeat. Cine images were reconstructed from the steady-state portion of 15° readouts using a low-rank plus sparse reconstruction. The T1 maps were fit using a projection onto convex sets approach from images reconstructed using slice profile-corrected dictionary learning. This strategy was evaluated in a phantom and 14 human subjects. RESULTS The self-gated signal demonstrated close agreement with the acquired electrocardiogram signal. The image quality for the proposed cine images and standard clinical balanced SSFP images were 4.31 (±0.50) and 4.65 (±0.30), respectively. The slice profile-corrected T1 values were similar to those of the inversion-recovery spin-echo technique in phantom, and had a higher global T1 value than that of MOLLI in human subjects. CONCLUSIONS Cine and T1 mapping using spiral acquisition with respiratory and cardiac self-gating successfully acquired T1 maps and cine images in a single acquisition without the need for electrocardiogram gating or breath-holding. This dual-excitation flip-angle approach provides a novel approach for measuring T1 while accounting for B1+ and slice profile effect on the apparent T1∗ .
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Affiliation(s)
- Ruixi Zhou
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Daniel S. Weller
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Junyu Wang
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Haris Jeelani
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - John P. Mugler
- Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
| | - Michael Salerno
- Cardiology, Radiology & Medical Imaging, Biomedical Engineering, University of Virginia Health System, Charlottesville, VA, United States
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Qi H, Cruz G, Botnar R, Prieto C. Synergistic multi-contrast cardiac magnetic resonance image reconstruction. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200197. [PMID: 33966456 DOI: 10.1098/rsta.2020.0197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Li Y, Wang Y, Qi H, Hu Z, Chen Z, Yang R, Qiao H, Sun J, Wang T, Zhao X, Guo H, Chen H. Deep learning-enhanced T 1 mapping with spatial-temporal and physical constraint. Magn Reson Med 2021; 86:1647-1661. [PMID: 33821529 DOI: 10.1002/mrm.28793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/07/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE To propose a reconstruction framework to generate accurate T1 maps for a fast MR T1 mapping sequence. METHODS A deep learning-enhanced T1 mapping method with spatial-temporal and physical constraint (DAINTY) was proposed. This method explicitly imposed low-rank and sparsity constraints on the multiframe T1 -weighted images to exploit the spatial-temporal correlation. A deep neural network was used to efficiently perform T1 mapping as well as denoise and reduce undersampling artifacts. Additionally, the physical constraint was used to build a bridge between low-rank and sparsity constraint and deep learning prior, so the benefits of constrained reconstruction and deep learning can be both available. The DAINTY method was trained on simulated brain data sets, but tested on real acquired phantom, 6 healthy volunteers, and 7 atherosclerosis patients, compared with the narrow-band k-space-weighted image contrast filter conjugate-gradient SENSE (NK-CS) method, kt-sparse-SENSE (kt-SS) method, and low-rank plus sparsity (L+S) method with least-squares T1 fitting and direct deep learning mapping. RESULTS The DAINTY method can generate more accurate T1 maps and higher-quality T1 -weighted images compared with other methods. For atherosclerosis patients, the intraplaque hemorrhage can be successfully detected. The computation speed of DAINTY was 10 times faster than traditional methods. Meanwhile, DAINTY can reconstruct images with comparable quality using only 50% of k-space data. CONCLUSION The proposed method can provide accurate T1 maps and good-quality T1 -weighted images with high efficiency.
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Affiliation(s)
- Yuze Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Zhangxuan Hu
- GE Healthcare, MR Research China, Beijing, China
| | - Zhensen Chen
- Vascular Imaging Lab and BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Runyu Yang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Huiyu Qiao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jie Sun
- GE Healthcare, MR Research China, Beijing, China
| | - Tao Wang
- Department of Neurology, Peking University Third Hospital, Beijing, China
| | - Xihai Zhao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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Eck BL, Flamm SD, Kwon DH, Tang WHW, Vasquez CP, Seiberlich N. Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2021; 122:11-22. [PMID: 33632415 PMCID: PMC8366914 DOI: 10.1016/j.pnmrs.2020.10.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/23/2020] [Accepted: 10/29/2020] [Indexed: 05/02/2023]
Abstract
Quantitative cardiac magnetic resonance has emerged in recent years as an approach for evaluating a range of cardiovascular conditions, with T1 and T2 mapping at the forefront of these developments. Cardiac Magnetic Resonance Fingerprinting (cMRF) provides a rapid and robust framework for simultaneous quantification of myocardial T1 and T2 in addition to other tissue properties. Since the advent of cMRF, a number of technical developments and clinical validation studies have been reported. This review provides an overview of cMRF, recent technical developments, healthy subject and patient studies, anticipated technical improvements, and potential clinical applications. Recent technical developments include slice profile and pulse efficiency corrections, improvements in image reconstruction, simultaneous multislice imaging, 3D whole-ventricle imaging, motion-resolved imaging, fat-water separation, and machine learning for rapid dictionary generation. Future technical developments in cMRF, such as B0 and B1 field mapping, acceleration of acquisition and reconstruction, imaging of patients with implanted devices, and quantification of additional tissue properties are also described. Potential clinical applications include characterization of infiltrative, inflammatory, and ischemic cardiomyopathies, tissue characterization in the left atrium and right ventricle, post-cardiac transplantation assessment, reduction of contrast material, pre-procedural planning for electrophysiology interventions, and imaging of patients with implanted devices.
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Affiliation(s)
- Brendan L Eck
- Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Scott D Flamm
- Heart and Vascular Institute and Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Deborah H Kwon
- Heart and Vascular Institute and Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - W H Wilson Tang
- Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Claudia Prieto Vasquez
- School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, UK.
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI 48109, USA.
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Busse A, Rajagopal R, Yücel S, Beller E, Öner A, Streckenbach F, Cantré D, Ince H, Weber MA, Meinel FG. Cardiac MRI-Update 2020. Radiologe 2021; 60:33-40. [PMID: 32385547 DOI: 10.1007/s00117-020-00687-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PURPOSE To review emerging techniques in cardiac magnetic resonance imaging (CMR) and their clinical applications with a special emphasis on new technologies, recent trials, and updated guidelines. TECHNOLOGICAL INNOVATIONS The utility of CMR has expanded with the development of new MR sequences, postprocessing techniques, and artificial intelligence-based technologies, which have substantially increased the spectrum, quality, and reliability of information that can be obtained by CMR. ESTABLISHED AND EMERGING INDICATIONS The CMR modality has become an irreplaceable tool for diagnosis, treatment guidance and follow-up of patients with ischemic heart disease, myocarditis, and cardiomyopathies. Its role has been further strengthened by recent trials and guidelines. Quantitative mapping techniques are increasingly used for tissue characterization and detection of diffuse myocardial changes including myocardial storage diseases. PRACTICAL RECOMMENDATIONS With state-of-the-art CMR sequences, postprocessing techniques and understanding of their interpretation, CMR makes invaluable contributions to provide state-of-the-art diagnostics and care for cardiac patients in a multidisciplinary team.
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Affiliation(s)
- Anke Busse
- Department of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Rengarajan Rajagopal
- Department of Cardiovascular Radiology and Endovascular Interventions, All India Institute of Medical Sciences, New Delhi, India
| | - Seyrani Yücel
- Department of Internal Medicine, Division of Cardiology, University Medical Center Rostock, Rostock, Germany
| | - Ebba Beller
- Department of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Alper Öner
- Department of Internal Medicine, Division of Cardiology, University Medical Center Rostock, Rostock, Germany
| | - Felix Streckenbach
- Department of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Daniel Cantré
- Department of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Hüseyin Ince
- Department of Internal Medicine, Division of Cardiology, University Medical Center Rostock, Rostock, Germany
| | - Marc-André Weber
- Department of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Felix G Meinel
- Department of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
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Küstner T, Bustin A, Jaubert O, Hajhosseiny R, Masci PG, Neji R, Botnar R, Prieto C. Fully self-gated free-running 3D Cartesian cardiac CINE with isotropic whole-heart coverage in less than 2 min. NMR IN BIOMEDICINE 2021; 34:e4409. [PMID: 32974984 DOI: 10.1002/nbm.4409] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 08/19/2020] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
PURPOSE To develop a novel fast water-selective free-breathing 3D Cartesian cardiac CINE scan with full self-navigation and isotropic whole-heart (WH) coverage. METHODS A free-breathing 3D Cartesian cardiac CINE scan with a water-selective balanced steady-state free precession and a continuous (non-ECG-gated) variable-density Cartesian sampling with spiral profile ordering, out-inward sampling and acquisition-adaptive alternating tiny golden and golden angle increment between spiral arms is proposed. Data is retrospectively binned based on respiratory and cardiac self-navigation signals. A translational respiratory-motion-corrected and cardiac-motion-resolved image is reconstructed with a multi-bin patch-based low-rank reconstruction (MB-PROST) within about 15 min. A respiratory-motion-resolved approach is also investigated. The proposed 3D Cartesian cardiac CINE is acquired in sagittal orientation in 1 min 50 s for 1.9 mm3 isotropic WH coverage. Left ventricular (LV) function parameters and image quality derived from a blinded reading of the proposed 3D CINE framework are compared against conventional multi-slice 2D CINE imaging in 10 healthy subjects and 10 patients with suspected cardiovascular disease. RESULTS The proposed framework provides free-breathing 3D cardiac CINE images with 1.9 mm3 spatial and about 45 ms temporal resolution in a short acquisition time (<2 min). LV function parameters derived from 3D CINE were in good agreement with 2D CINE (10 healthy subjects and 10 patients). Bias and confidence intervals were obtained for end-systolic volume, end-diastolic volume and ejection fraction of 0.1 ± 3.5 mL, -0.6 ± 8.2 mL and -0.1 ± 2.2%, respectively. CONCLUSION The proposed framework enables isotropic 3D Cartesian cardiac CINE under free breathing for fast assessment of cardiac anatomy and function.
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Affiliation(s)
- Thomas Küstner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Pier Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, 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, St. Thomas' Hospital, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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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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Bush MA, Pan Y, Jin N, Liu Y, Varghese J, Ahmad R, Simonetti OP. Prospective correction of patient-specific respiratory motion in myocardial T 1 and T 2 mapping. Magn Reson Med 2020; 85:855-867. [PMID: 32851676 DOI: 10.1002/mrm.28475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 06/29/2020] [Accepted: 07/22/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE Respiratory motion in cardiovascular MRI presents a challenging problem with many potential solutions. Current approaches require breath-holds, apply retrospective image registration, or significantly increase scan time by respiratory gating. Myocardial T1 and T2 mapping techniques are particularly sensitive to motion as they require multiple source images to be accurately aligned prior to the estimation of tissue relaxation. We propose a patient-specific prospective motion correction (PROCO) strategy that corrects respiratory motion on the fly with the goal of reducing the spatial variation of myocardial parametric mapping techniques. METHODS A rapid, patient-specific training scan was performed to characterize respiration-induced motion of the heart relative to a diaphragmatic navigator, and a parametric mapping pulse sequence utilized the resulting motion model to prospectively update the scan plane in real-time. Midventricular short-axis T1 and T2 maps were acquired under breath-hold or free-breathing conditions with and without PROCO in 7 healthy volunteers and 3 patients. T1 and T2 were measured in 6 segments and compared to reference standard breath-hold measurements using Bland-Altman analysis. RESULTS PROCO significantly reduced the spatial variation of parametric maps acquired during free-breathing, producing limits of agreement of -47.16 to 30.98 ms (T1 ) and -1.35 to 4.02 ms (T2 ), compared to -67.77 to 74.34 ms (T1 ) and -2.21 to 5.62 ms (T2 ) for free-breathing acquisition without PROCO. CONCLUSION Patient-specific respiratory PROCO method significantly reduced the spatial variation of myocardial T1 and T2 mapping, while allowing for 100% efficient free-breathing acquisitions.
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Affiliation(s)
- Michael A Bush
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Yue Pan
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA
| | - Ning Jin
- Cardiovascular MR R&D, Siemens Medical Solutions USA Inc, Columbus, Ohio, USA
| | - Yingmin Liu
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA
| | - Juliet Varghese
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA
| | - Rizwan Ahmad
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.,Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.,Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Orlando P Simonetti
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.,Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.,Internal Medicine, The Ohio State University, Columbus, Ohio, USA.,Radiology, The Ohio State University, Columbus, Ohio, USA
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30
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Hamilton JI, Jiang Y, Eck B, Griswold M, Seiberlich N. Cardiac cine magnetic resonance fingerprinting for combined ejection fraction, T 1 and T 2 quantification. NMR IN BIOMEDICINE 2020; 33:e4323. [PMID: 32500541 PMCID: PMC7772953 DOI: 10.1002/nbm.4323] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 05/07/2023]
Abstract
This study introduces a technique called cine magnetic resonance fingerprinting (cine-MRF) for simultaneous T1 , T2 and ejection fraction (EF) quantification. Data acquired with a free-running MRF sequence are retrospectively sorted into different cardiac phases using an external electrocardiogram (ECG) signal. A low-rank reconstruction with a finite difference sparsity constraint along the cardiac motion dimension yields images resolved by cardiac phase. To improve SNR and precision in the parameter maps, these images are nonrigidly registered to the same phase and matched to a dictionary to generate T1 and T2 maps. Cine images for computing left ventricular volumes and EF are also derived from the same data. Cine-MRF was tested in simulations using a numerical relaxation phantom. Phantom and in vivo scans of 19 subjects were performed at 3 T during a 10.9 seconds breath-hold with an in-plane resolution of 1.6 x 1.6 mm2 and 24 cardiac phases. Left ventricular EF values obtained with cine-MRF agreed with the conventional cine images (mean bias -1.0%). Average myocardial T1 times in diastole/systole were 1398/1391 ms with cine-MRF, 1394/1378 ms with ECG-triggered cardiac MRF (cMRF) and 1234/1212 ms with MOLLI; and T2 values were 30.7/30.3 ms with cine-MRF, 32.6/32.9 ms with ECG-triggered cMRF and 37.6/41.0 ms with T2 -prepared FLASH. Cine-MRF and ECG-triggered cMRF relaxation times were in good agreement. Cine-MRF T1 values were significantly longer than MOLLI, and cine-MRF T2 values were significantly shorter than T2 -prepared FLASH. In summary, cine-MRF can potentially streamline cardiac MRI exams by combining left ventricle functional assessment and T1 -T2 mapping into one time-efficient acquisition.
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Affiliation(s)
- Jesse I. Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Corresponding author at 1137 Catherine Street, Room 1590B, Ann Arbor, MI 48109, JI Hamilton –
| | - Yun Jiang
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Brendan Eck
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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31
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Küstner T, Bustin A, Jaubert O, Hajhosseiny R, Masci PG, Neji R, Botnar R, Prieto C. Isotropic 3D Cartesian single breath-hold CINE MRI with multi-bin patch-based low-rank reconstruction. Magn Reson Med 2020; 84:2018-2033. [PMID: 32250492 DOI: 10.1002/mrm.28267] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/08/2020] [Accepted: 03/09/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To develop a novel acquisition and reconstruction framework for isotropic 3D Cartesian cardiac CINE within a single breath-hold for left ventricle (LV) and whole-heart coverage. METHODS A variable-density Cartesian acquisition with spiral profile ordering, out-inward sampling, and acquisition-adaptive alternating tiny golden/golden angle increment between spiral arms is proposed to provide incoherent and nonredundant sampling within and among cardiac phases. A novel multi-bin patch-based low-rank reconstruction, named MB-PROST, is proposed to exploit redundant information on a local (within a patch), nonlocal (similar patches within a spatial neighborhood), and temporal (among all cardiac phases) scale with an implicit motion alignment among patches. The proposed multi-bin patch-based low-rank reconstruction reconstruction is compared against compressed sensing reconstruction, whereas LV function parameters derived from the proposed 3D CINE framework are compared against those estimated from conventional multislice 2D CINE imaging in 10 healthy subjects and 15 patients. RESULTS The proposed framework provides 3D cardiac CINE images with high spatial (1.9 mm3 ) and temporal resolution (˜50 ms) in a single breath-hold of ˜20 s for LV and ˜26 s for whole-heart coverage in healthy subjects. Shorter breath-hold durations of ˜13 to 15 s are feasible for LV coverage with slightly anisotropic resolution (1.9 × 1.9 × 2.5 mm) in patients. LV function parameters derived from 3D CINE were in good agreement with 2D CINE, with a bias of -0.1 mL/0.1 mL, -0.9 mL/-1.0 mL, -0.1%/-0.8%; and confidence intervals of ±1.7 mL/±3.7 mL, ±1.2 mL/±2.6 mL, and ±1.2%/±3.6% (10 healthy subjects/15 patients) for end-systolic volume, end-diastolic volume, and ejection fraction, respectively. CONCLUSION The proposed framework enables 3D isotropic cardiac CINE in a single breath-hold scan of ˜20 s/˜26 s for LV/whole-heart coverage, showing good agreement with clinical 2D CINE scans in terms of LV functional assessment.
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Affiliation(s)
- Thomas Küstner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Pier Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, 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, St. Thomas' Hospital, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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32
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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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33
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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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