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Nazir MS, Shome J, Villa ADM, Ryan M, Kassam Z, Razavi R, Kozerke S, Ismail TF, Perera D, Chiribiri A, Plein S. 2D high resolution vs. 3D whole heart myocardial perfusion cardiovascular magnetic resonance. Eur Heart J Cardiovasc Imaging 2022; 23:811-819. [PMID: 34179941 PMCID: PMC9159745 DOI: 10.1093/ehjci/jeab103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Indexed: 11/21/2022] Open
Abstract
AIMS Developments in myocardial perfusion cardiovascular magnetic resonance (CMR) allow improvements in spatial resolution and/or myocardial coverage. Whole heart coverage may provide the most accurate assessment of myocardial ischaemic burden, while high spatial resolution is expected to improve detection of subendocardial ischaemia. The objective of this study was to compare myocardial ischaemic burden as depicted by 2D high resolution and 3D whole heart stress myocardial perfusion in patients with coronary artery disease. METHODS AND RESULTS Thirty-eight patients [age 61 ± 8 (21% female)] underwent 2D high resolution (spatial resolution 1.2 mm2) and 3D whole heart (in-plane spatial resolution 2.3 mm2) stress CMR at 3-T in randomized order. Myocardial ischaemic burden (%) was visually quantified as perfusion defect at peak stress perfusion subtracted from subendocardial myocardial scar and expressed as a percentage of the myocardium. Median myocardial ischaemic burden was significantly higher with 2D high resolution compared with 3D whole heart [16.1 (2.0-30.6) vs. 13.4 (5.2-23.2), P = 0.004]. There was excellent agreement between myocardial ischaemic burden (intraclass correlation coefficient 0.81; P < 0.0001), with mean ratio difference between 2D high resolution vs. 3D whole heart 1.28 ± 0.67 (95% limits of agreement -0.03 to 2.59). When using a 10% threshold for a dichotomous result for presence or absence of significant ischaemia, there was moderate agreement between the methods (κ = 0.58, P < 0.0001). CONCLUSION 2D high resolution and 3D whole heart myocardial perfusion stress CMR are comparable for detection of ischaemia. 2D high resolution gives higher values for myocardial ischaemic burden compared with 3D whole heart, suggesting that 2D high resolution is more sensitive for detection of ischaemia.
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Affiliation(s)
- Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Joy Shome
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Adriana D M Villa
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Matthew Ryan
- British Heart Foundation Centre of Excellence and National Institute for Health Research Biomedical Research Centre at the School of Cardiovascular Medicine and Sciences, Kings College London, London, UK
| | - Ziyan Kassam
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Tevfik F Ismail
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Divaka Perera
- British Heart Foundation Centre of Excellence and National Institute for Health Research Biomedical Research Centre at the School of Cardiovascular Medicine and Sciences, Kings College London, London, UK
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
| | - Sven Plein
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, 4th Floor Lambeth Wing, St Thomas’ Hospital, Westminster Bridge Road, London SW1 7EH, UK
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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McElroy S, Ferrazzi G, Nazir MS, Evans C, Ferreira J, Bosio F, Mughal N, Kunze KP, Neji R, Speier P, Stäb D, Ismail TF, Masci PG, Villa ADM, Razavi R, Chiribiri A, Roujol S. Simultaneous multislice steady-state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T. Magn Reson Med 2022; 88:663-675. [PMID: 35344593 PMCID: PMC9310832 DOI: 10.1002/mrm.29229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/27/2022]
Abstract
Purpose To implement and evaluate a simultaneous multi‐slice balanced SSFP (SMS‐bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm2) at 1.5T. Methods A preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF‐CAIPI with gradient‐controlled local Larmor adjustment (GC‐LOLA) in the presence of fat. A nine‐slice SMS‐bSSFP sequence using RF‐CAIPI with GC‐LOLA with high spatial resolution (1.4 × 1.4 mm2) and a conventional three‐slice sequence with conventional spatial resolution (1.9 × 1.9 mm2) were then acquired in 10 patients under rest conditions. Qualitative assessment was performed to assess image quality and perceived signal‐to‐noise ratio (SNR) on a 4‐point scale (0: poor image quality/low SNR; 3: excellent image quality/high SNR), and the number of myocardial segments with diagnostic image quality was recorded. Quantitative measurements of myocardial sharpness and upslope index were performed. Results Fat signal leakage was significantly higher for blipped CAIPI than for RF‐CAIPI with GC‐LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS‐bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter‐reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional sequence (0.37 ± 0.08 vs 0.32 ± 0.05, p < 0.001). There was no significant difference between measurements of upslope index for the SMS and conventional sequences (0.11 ± 0.04 vs. 0.11 ± 0.03, p = 0.84). Conclusion SMS‐bSSFP with multiband factor 3 and compressed sensing reconstruction enables cardiac MR perfusion imaging with three‐fold increased spatial coverage and improved myocardial sharpness compared to a conventional sequence, without compromising perceived SNR, image quality, upslope index or number of diagnostic segments.
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Affiliation(s)
- Sarah McElroy
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | | | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Carl Evans
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Joana Ferreira
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Filippo Bosio
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Nabila Mughal
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, England, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, England, UK
| | - Peter Speier
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Daniel Stäb
- MR Research Collaborations, Siemens Healthcare Limited, Melbourne, Australia
| | - Tevfik F Ismail
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Pier Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Adriana D M Villa
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
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Le J, Tian Y, Mendes J, Wilson B, Ibrahim M, DiBella E, Adluru G. Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net. Magn Reson Imaging 2021; 83:178-188. [PMID: 34428512 PMCID: PMC8493758 DOI: 10.1016/j.mri.2021.08.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 08/12/2021] [Accepted: 08/13/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method. METHODS Dynamic contrast enhanced (DCE) radial SMS myocardial perfusion data were obtained from 20 subjects who were scanned at rest and/or stress with or without ECG gating using a saturation recovery radial CAIPI turboFLASH sequence. Input to the networks consisted of complex coil combined images reconstructed using the inverse Fourier transform of undersampled radial SMS k-space data. Ground truth images were reconstructed using the PT-STCR pipeline. The performance of the residual booster 3D U-Net was tested by comparing it to state-of-the-art network architectures including MoDL, CRNN-MRI, and other U-Net variants. RESULTS Results demonstrate significant improvements in speed requiring approximately 8 seconds to reconstruct one radial SMS dataset which is approximately 200 times faster than the PT-STCR method. Images reconstructed with the residual booster 3D U-Net retain quality of ground truth PT-STCR images (0.963 SSIM/40.238 PSNR/0.147 NRMSE). The residual booster 3D U-Net has superior performance compared to existing network architectures in terms of image quality, temporal dynamics, and reconstruction time. CONCLUSION Residual and booster learning combined with the 3D U-Net architecture was shown to be an effective network for reconstructing high-quality images from undersampled radial SMS datasets while bypassing the reconstruction time of the PT-STCR method.
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Affiliation(s)
- Johnathan Le
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Ye Tian
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA; Department of Physics and Astronomy, University of Utah, Salt Lake City, UT, USA; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jason Mendes
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA
| | - Brent Wilson
- Department of Cardiology, University of Utah, Salt Lake City, UT, USA
| | - Mark Ibrahim
- Department of Cardiology, University of Utah, Salt Lake City, UT, USA
| | - Edward DiBella
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Ganesh Adluru
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
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Pan JA, Robinson AA, Yang Y, Lozano PR, McHugh S, Holland EM, Meyer CH, Taylor AM, Kramer CM, Salerno M. Diagnostic Accuracy of Spiral Whole-Heart Quantitative Adenosine Stress Cardiovascular Magnetic Resonance With Motion Compensated L1-SPIRIT. J Magn Reson Imaging 2021; 54:1268-1279. [PMID: 33822426 DOI: 10.1002/jmri.27620] [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: 12/20/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Variable density spiral (VDS) pulse sequences with motion compensated compressed sensing (MCCS) reconstruction allow for whole-heart quantitative assessment of myocardial perfusion but are not clinically validated. PURPOSE Assess performance of whole-heart VDS quantitative stress perfusion with MCCS to detect obstructive coronary artery disease (CAD). STUDY TYPE Prospective cross sectional. POPULATION Twenty-five patients with chest pain and known or suspected CAD and nine normal subjects. FIELD STRENGTH/SEQUENCE Segmented steady-state free precession (SSFP) sequence, segmented phase sensitive inversion recovery sequence for late gadolinium enhancement (LGE) imaging and VDS sequence at 1.5 T for rest and stress quantitative perfusion at eight short-axis locations. ASSESSMENT Stenosis was defined as ≥50% by quantitative coronary angiography (QCA). Visual and quantitative analysis of MRI data was compared to QCA. Quantitative analysis assessed average myocardial perfusion reserve (MPR), average stress myocardial blood flow (MBF), and lowest stress MBF of two contiguous myocardial segments. Ischemic burden was measured visually and quantitatively. STATISTICAL TESTS Student's t-test, McNemar's test, chi-square statistic, linear mixed-effects model, and area under receiver-operating characteristic curve (ROC). RESULTS Per-patient visual analysis demonstrated a sensitivity of 84% (95% confidence interval [CI], 60%-97%) and specificity of 83% [95% CI, 36%-100%]. There was no significant difference between per-vessel visual and quantitative analysis for sensitivity (69% [95% CI, 51%-84%] vs. 77% [95% CI, 60%-90%], P = 0.39) and specificity (88% [95% CI, 73%-96%] vs. 80% [95% CI, 64%-91%], P = 0.75). Per-vessel quantitative analysis ROC showed no significant difference (P = 0.06) between average MPR (0.68 [95% CI, 0.56-0.81]), average stress MBF (0.74 [95% CI, 0.63-0.86]), and lowest stress MBF (0.79 [95% CI, 0.69-0.90]). Visual and quantitative ischemic burden measurements were comparable (P = 0.85). DATA CONCLUSION Whole-heart VDS stress perfusion demonstrated good diagnostic accuracy and ischemic burden evaluation. No significant difference was seen between visual and quantitative diagnostic performance and ischemic burden measurements. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jonathan A Pan
- Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Austin A Robinson
- Division of Cardiovascular Diseases, Division of Radiology, Scripps Clinic, La Jolla, California, USA
| | - Yang Yang
- Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA.,Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patricia Rodriguez Lozano
- Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Stephen McHugh
- Department of Internal Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA
| | - Eric M Holland
- Division of Cardiology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Craig H Meyer
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA.,Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Angela M Taylor
- Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Christopher M Kramer
- Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA.,Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Michael Salerno
- Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA.,Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA.,Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
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Reiber JHC, Pereira GTR, Bezerra HG, De Sutter J, Schoenhagen P, Stillman AE, Van de Veire NRL. Cardiovascular imaging 2018 in the International Journal of Cardiovascular Imaging. Int J Cardiovasc Imaging 2019; 35:1175-1188. [DOI: 10.1007/s10554-019-01579-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Tian Y, Mendes J, Pedgaonkar A, Ibrahim M, Jensen L, Schroeder JD, Wilson B, DiBella EVR, Adluru G. Feasibility of multiple-view myocardial perfusion MRI using radial simultaneous multi-slice acquisitions. PLoS One 2019; 14:e0211738. [PMID: 30742641 PMCID: PMC6370206 DOI: 10.1371/journal.pone.0211738] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 01/18/2019] [Indexed: 11/18/2022] Open
Abstract
Purpose Dynamic contrast enhanced MRI of the heart typically acquires 2–4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasibility of using radial simultaneous multi-slice (SMS) technique to achieve SA, 2-chamber and/or 4-chamber long-axis (2CH LA and/or 4CH LA) coverage with and without electrocardiography (ECG) gating using a motion-robust reconstruction framework. Methods 12 subjects were scanned at rest and/or stress, free breathing, with or without ECG gating. Multiple sets of radial SMS k-space were acquired within each cardiac cycle, and each SMS set sampled 3 parallel slices that were either SA, 2CH LA, or 4CH LA slices. The radial data was interpolated onto Cartesian space using an SMS GRAPPA operator gridding method. Self-gating and respiratory states binning of the data were done. The binning information as well as a pixel tracking spatiotemporal constrained reconstruction method were applied to obtain motion-robust image reconstructions. Reconstructions with and without the pixel tracking method were compared for signal-to-noise ratio and contrast-to-noise ratio. Results Full coverage of the heart (at least 3 SA and 3 LA slices) during the first pass of contrast at every heartbeat was achieved by using the radial SMS acquisition. The proposed pixel tracking reconstruction improves the average SNR and CNR by 21% and 30% respectively, and reduces temporal blurring for both gated and ungated acquisitions. Conclusion Acquiring simultaneous multi-slice SA, 2CH LA and/or 4CH LA myocardial perfusion images in every heartbeat is feasible in both gated and ungated acquisitions. This can add confidence when detecting and characterizing coronary artery disease by revealing ischemia in different views, and by providing apical coverage that is improved relative to SA slices alone. The proposed pixel tracking framework improves the reconstruction while adding little computational cost.
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Affiliation(s)
- Ye Tian
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
- Department of Physics and Astronomy, University of Utah, Salt Lake City, Utah, United States of America
| | - Jason Mendes
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Apoorva Pedgaonkar
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Mark Ibrahim
- Division of Cardiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Leif Jensen
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Joyce D. Schroeder
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Brent Wilson
- Division of Cardiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Edward V. R. DiBella
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Ganesh Adluru
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
- * E-mail:
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