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Eslami P, Seo JH, Lardo AC, Chen MY, Mittal R. Flow Dynamics in the Aortic Arch and Its Effect on the Arterial Input Function in Cardiac Computed Tomography. J Biomech Eng 2019; 141:2728067. [PMID: 30840028 DOI: 10.1115/1.4043076] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Indexed: 02/04/2023]
Abstract
The arterial input function (AIF)-time-density curve (TDC) of contrast at the coronary ostia-plays a central role in contrast enhanced computed tomography angiography (CTA). This study employs computational modeling in a patient-specific aorta to investigate mixing and dispersion of contrast in the aortic arch (AA) and to compare the TDCs in the coronary ostium and the descending aorta. Here, we examine the validity of the use of TDC in the descending aorta as a surrogate for the AIF. Computational fluid dynamics was used to study hemodynamics and contrast dispersion in a CTA-based patient model of the aorta. Variations in TDC between the aortic root, through the AA and at the descending aorta and the effect of flow patterns on contrast dispersion was studied via post-processing of the results. Simulations showed complex unsteady patterns of contrast mixing and dispersion in the AA that are driven by the pulsatile flow. However, despite the relatively long intra-aortic distance between the coronary ostia and the descending aorta, the TDCs at these two locations were similar in terms of rise-time and up-slope, and the time lag between the two TDCs was 0.19 seconds. TDC in the descending aorta is an accurate analog of the AIF. Methods that use quantitative metrics such as rise-time and slope of the AIF to estimate coronary flowrate and myocardial ischemia can continue with the current practice of using the TDC at the descending aorta as a surrogate for the AIF.
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Affiliation(s)
- Parastou Eslami
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, MA 02114
| | - Jung-Hee Seo
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Albert C Lardo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Marcus Y Chen
- National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD 20892
| | - Rajat Mittal
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21287
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Ziegler M, Lantz J, Ebbers T, Dyverfeldt P. Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI. Magn Reson Med 2016; 77:2310-2319. [PMID: 27350049 DOI: 10.1002/mrm.26308] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 03/29/2016] [Accepted: 05/23/2016] [Indexed: 11/10/2022]
Abstract
PURPOSE To explore the use of MR-estimated turbulence quantities for the assessment of turbulent flow effects on the vessel wall. METHODS Numerical velocity data for two patient-derived models was obtained using computational fluid dynamics (CFD) for two physiological flow rates. The four-dimensional (4D) Flow MRI measurements were simulated at three different spatial resolutions and used to investigate the estimation of turbulent wall shear stress (tWSS) using the intravoxel standard deviation (IVSD) of velocity and turbulent kinetic energy (TKE) estimated near the vessel wall. RESULTS Accurate estimation of tWSS using the IVSD is limited by the spatial resolution achievable with 4D Flow MRI. TKE, estimated near the wall, has a strong linear relationship to the tWSS (mean R2 = 0.84). Near-wall TKE estimates from MR simulations have good agreement to CFD-derived ground truth (mean R2 = 0.90). Maps of near-wall TKE have strong visual correspondence to tWSS. CONCLUSION Near-wall estimation of TKE permits assessment of relative maps of tWSS, but direct estimation of tWSS is challenging due to limitations in spatial resolution. Assessment of tWSS and near-wall TKE may open new avenues for analysis of different pathologies. Magn Reson Med 77:2310-2319, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Magnus Ziegler
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Jonas Lantz
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Petter Dyverfeldt
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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4
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Callaghan FM, Kozor R, Sherrah AG, Vallely M, Celermajer D, Figtree GA, Grieve SM. Use of multi-velocity encoding 4D flow MRI to improve quantification of flow patterns in the aorta. J Magn Reson Imaging 2015; 43:352-63. [PMID: 26130421 DOI: 10.1002/jmri.24991] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 06/17/2015] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To show that the use of a multi-velocity encoding (VENC) 4D-flow approach offers significant improvements in the characterization of complex flow in the aorta. Four-dimensional flow magnetic resonance imaging (MRI) (4D-flow) can be used to measure complex flow patterns and dynamics in the heart and major vessels. The quality of the information derived from these measures is dependent on the accuracy of the vector field, which is limited by the vector-to-noise ratio. MATERIALS AND METHODS A 4D-flow protocol involving three different VENC values of 150, 60, and 20 cm/s was performed on six control subjects and nine patients with type-B chronic aortic dissection at 3T MRI. Data were processed using a single VENC value (150 cm/s) or using a fused dataset that selected the lowest appropriate VENC for each voxel. Performance was analyzed by measuring spatial vector angular correlation, magnitude correlation, temporal vector conservation, and "real-world" streamline tracing performance. RESULTS The multi-VENC approach provided a 31% improvement in spatial and 53% improvement in temporal precision of velocity vector measurements during the mid-late diastolic period, where 99% of the flow vectors in the normal aorta are below 20 cm/s. In low-flow conditions this resulted in practical improvements of greater than 50% in pathline tracking and streamline tracing quantified by streamline curvature measurements. CONCLUSION A multi-VENC 4D-flow approach provides accurate vector data across normal physiological velocities observed in the aorta, dramatically improving outputs such as pathline tracking, streamline estimation, and further advanced analyses.
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Affiliation(s)
- Fraser M Callaghan
- Sydney Translational Imaging Laboratory, Sydney Medical School & Charles Perkins Centre, University of Sydney, Sydney, Australia.,Heart Research Institute, Newtown, Sydney, Australia
| | - Rebecca Kozor
- Sydney Translational Imaging Laboratory, Sydney Medical School & Charles Perkins Centre, University of Sydney, Sydney, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, Australia.,North Shore Heart Research Group, Kolling Institute, University of Sydney, Australia
| | - Andrew G Sherrah
- Sydney Translational Imaging Laboratory, Sydney Medical School & Charles Perkins Centre, University of Sydney, Sydney, Australia.,The Baird Institute, Camperdown, Australia.,Cardiothoracic Surgical Unit, Royal Prince Alfred Hospital, Sydney, Australia
| | - Michael Vallely
- The Baird Institute, Camperdown, Australia.,Cardiothoracic Surgical Unit, Royal Prince Alfred Hospital, Sydney, Australia
| | - David Celermajer
- Heart Research Institute, Newtown, Sydney, Australia.,Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Gemma A Figtree
- Sydney Translational Imaging Laboratory, Sydney Medical School & Charles Perkins Centre, University of Sydney, Sydney, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, Australia.,North Shore Heart Research Group, Kolling Institute, University of Sydney, Australia
| | - Stuart M Grieve
- Sydney Translational Imaging Laboratory, Sydney Medical School & Charles Perkins Centre, University of Sydney, Sydney, Australia.,Heart Research Institute, Newtown, Sydney, Australia.,Department of Radiology, Royal Prince Alfred Hospital, Sydney, Australia
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6
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Lagrangian postprocessing of computational hemodynamics. Ann Biomed Eng 2014; 43:41-58. [PMID: 25059889 DOI: 10.1007/s10439-014-1070-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Accepted: 07/11/2014] [Indexed: 10/25/2022]
Abstract
Recent advances in imaging, modeling, and computing have rapidly expanded our capabilities to model hemodynamics in the large vessels (heart, arteries, and veins). This data encodes a wealth of information that is often under-utilized. Modeling (and measuring) blood flow in the large vessels typically amounts to solving for the time-varying velocity field in a region of interest. Flow in the heart and larger arteries is often complex, and velocity field data provides a starting point for investigating the hemodynamics. This data can be used to perform Lagrangian particle tracking, and other Lagrangian-based postprocessing. As described herein, Lagrangian methods are necessary to understand inherently transient hemodynamic conditions from the fluid mechanics perspective, and to properly understand the biomechanical factors that lead to acute and gradual changes of vascular function and health. The goal of the present paper is to review Lagrangian methods that have been used in post-processing velocity data of cardiovascular flows.
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