1
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Bissell MM, Raimondi F, Ait Ali L, Allen BD, Barker AJ, Bolger A, Burris N, Carhäll CJ, Collins JD, Ebbers T, Francois CJ, Frydrychowicz A, Garg P, Geiger J, Ha H, Hennemuth A, Hope MD, Hsiao A, Johnson K, Kozerke S, Ma LE, Markl M, Martins D, Messina M, Oechtering TH, van Ooij P, Rigsby C, Rodriguez-Palomares J, Roest AAW, Roldán-Alzate A, Schnell S, Sotelo J, Stuber M, Syed AB, Töger J, van der Geest R, Westenberg J, Zhong L, Zhong Y, Wieben O, Dyverfeldt P. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. J Cardiovasc Magn Reson 2023; 25:40. [PMID: 37474977 PMCID: PMC10357639 DOI: 10.1186/s12968-023-00942-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/30/2023] [Indexed: 07/22/2023] Open
Abstract
Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.
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Affiliation(s)
- Malenka M Bissell
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), LIGHT Laboratories, Clarendon Way, University of Leeds, Leeds, LS2 9NL, UK.
| | | | - Lamia Ait Ali
- Institute of Clinical Physiology CNR, Massa, Italy
- Foundation CNR Tuscany Region G. Monasterio, Massa, Italy
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alex J Barker
- Department of Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Center, Aurora, USA
| | - Ann Bolger
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Nicholas Burris
- Department of Radiology, University of Michigan, Ann Arbor, USA
| | - Carl-Johan Carhäll
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Tino Ebbers
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Alex Frydrychowicz
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Julia Geiger
- Department of Diagnostic Imaging, University Children's Hospital, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Hojin Ha
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, South Korea
| | - Anja Hennemuth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael D Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Albert Hsiao
- Department of Radiology, University of California, San Diego, CA, USA
| | - Kevin Johnson
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Liliana E Ma
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Duarte Martins
- Department of Pediatric Cardiology, Hospital de Santa Cruz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Marci Messina
- Department of Radiology, Northwestern Medicine, Chicago, IL, USA
| | - Thekla H Oechtering
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Pim van Ooij
- Department of Radiology & Nuclear Medicine, Amsterdam Cardiovascular Sciences, Amsterdam Movement Sciences, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Department of Pediatric Cardiology, Division of Pediatrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cynthia Rigsby
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Imaging, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jose Rodriguez-Palomares
- Department of Cardiology, Hospital Universitari Vall d´Hebron,Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red-CV, CIBER CV, Madrid, Spain
| | - Arno A W Roest
- Department of Pediatric Cardiology, Willem-Alexander's Children Hospital, Leiden University Medical Center and Center for Congenital Heart Defects Amsterdam-Leiden, Leiden, The Netherlands
| | | | - Susanne Schnell
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Physics, Institute of Physics, University of Greifswald, Greifswald, Germany
| | - Julio Sotelo
- School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile
| | - Matthias Stuber
- Département de Radiologie Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Ali B Syed
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Johannes Töger
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos Westenberg
- CardioVascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Liang Zhong
- National Heart Centre Singapore, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Yumin Zhong
- Department of Radiology, School of Medicine, Shanghai Children's Medical Center Affiliated With Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Oliver Wieben
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Petter Dyverfeldt
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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2
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Dirix P, Buoso S, Peper ES, Kozerke S. Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves. Sci Rep 2022; 12:16004. [PMID: 36163357 PMCID: PMC9513106 DOI: 10.1038/s41598-022-20121-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier-Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.
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Affiliation(s)
- Pietro Dirix
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | - Stefano Buoso
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Eva S Peper
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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3
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Weisskopf M, Glaus L, Trimmel NE, Hierweger MM, Leuthardt AS, Kukucka M, Stolte T, Stoeck CT, Falk V, Emmert MY, Kofler M, Cesarovic N. Dos and don'ts in large animal models of aortic insufficiency. Front Vet Sci 2022; 9:949410. [PMID: 36118338 PMCID: PMC9478759 DOI: 10.3389/fvets.2022.949410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/22/2022] [Indexed: 11/14/2022] Open
Abstract
Aortic insufficiency caused by paravalvular leakage (PVL) is one of the most feared complications following transcatheter aortic valve replacement (TAVI) in patients. Domestic pigs (Sus scrofa domestica) are a popular large animal model to study such conditions and develop novel diagnostic and therapeutic techniques. However, the models based on prosthetic valve implantation are time intensive, costly, and often hamper further hemodynamic measurements such as PV loop and 4D MRI flow by causing implantation-related wall motion abnormalities and degradation of MR image quality. This study describes in detail, the establishment of a minimally invasive porcine model suitable to study the effects of mild-to-moderate “paravalvular“ aortic regurgitation on left ventricular (LV) performance and blood flow patterns, particularly under the influence of altered afterload, preload, inotropic state, and heart rate. Six domestic pigs (Swiss large white, female, 60–70 kg of body weight) were used to establish this model. The defects on the hinge point of aortic leaflets and annulus were created percutaneously by the pierce-and-dilate technique either in the right coronary cusp (RCC) or in the non-coronary cusp (NCC). The hemodynamic changes as well as LV performance were recorded by PV loop measurements, while blood flow patterns were assessed by 4D MRI. LV performance was additionally challenged by pharmaceutically altering cardiac inotropy, chronotropy, and afterload. The presented work aims to elaborate the dos and don'ts in porcine models of aortic insufficiency and intends to steepen the learning curve for researchers planning to use this or similar models by giving valuable insights ranging from animal selection to vascular access choices, placement of PV Loop catheter, improvement of PV loop data acquisition and post-processing and finally the induction of paravalvular regurgitation of the aortic valve by a standardized and reproducible balloon induced defect in a precisely targeted region of the aortic valve.
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Affiliation(s)
- Miriam Weisskopf
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lukas Glaus
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Nina E. Trimmel
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Melanie M. Hierweger
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andrea S. Leuthardt
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marian Kukucka
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Thorald Stolte
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Christian T. Stoeck
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Volkmar Falk
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- Department of Cardiovascular Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Maximilian Y. Emmert
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- Department of Cardiovascular Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Kofler
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Nikola Cesarovic
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- *Correspondence: Nikola Cesarovic
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4
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Ha H, Huh HK, Park KJ, Dyverfeldt P, Ebbers T, Kim DH, Yang DH. In-vitro and In-Vivo Assessment of 4D Flow MRI Reynolds Stress Mapping for Pulsatile Blood Flow. Front Bioeng Biotechnol 2021; 9:774954. [PMID: 34950643 PMCID: PMC8691458 DOI: 10.3389/fbioe.2021.774954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/16/2021] [Indexed: 02/03/2023] Open
Abstract
Imaging hemodynamics play an important role in the diagnosis of abnormal blood flow due to vascular and valvular diseases as well as in monitoring the recovery of normal blood flow after surgical or interventional treatment. Recently, characterization of turbulent blood flow using 4D flow magnetic resonance imaging (MRI) has been demonstrated by utilizing the changes in signal magnitude depending on intravoxel spin distribution. The imaging sequence was extended with a six-directional icosahedral (ICOSA6) flow-encoding to characterize all elements of the Reynolds stress tensor (RST) in turbulent blood flow. In the present study, we aimed to demonstrate the feasibility of full RST analysis using ICOSA6 4D flow MRI under physiological conditions. First, the turbulence analysis was performed through in vitro experiments with a physiological pulsatile flow condition. Second, a total of 12 normal subjects and one patient with severe aortic stenosis were analyzed using the same sequence. The in-vitro study showed that total turbulent kinetic energy (TKE) was less affected by the signal-to-noise ratio (SNR), however, maximum principal turbulence shear stress (MPTSS) and total turbulence production (TP) had a noise-induced bias. Smaller degree of the bias was observed for TP compared to MPTSS. In-vivo study showed that the subject-variability on turbulence quantification was relatively low for the consistent scan protocol. The in vivo demonstration of the stenosis patient showed that the turbulence analysis could clearly distinguish the difference in all turbulence parameters as they were at least an order of magnitude larger than those from the normal subjects.
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Affiliation(s)
- Hojin Ha
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, South Korea
| | - Hyung Kyu Huh
- Daegu-Gyeongbuk Medical Innovation Foundation, Medical Device Development Center, Daegu, South Korea
| | - Kyung Jin Park
- Department of Electrical and Electronic Engineering, Yonsei Univeristy, Seoul, South Korea.,Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Petter Dyverfeldt
- Department of Health, Medicine and Caring Science, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Department of Health, Medicine and Caring Science, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Dae-Hee Kim
- Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Dong Hyun Yang
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
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5
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Dillinger H, McGrath C, Guenthner C, Kozerke S. Fundamentals of turbulent flow spectrum imaging. Magn Reson Med 2021; 87:1231-1249. [PMID: 34786764 PMCID: PMC9299145 DOI: 10.1002/mrm.29001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 08/12/2021] [Accepted: 08/18/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce a mathematical framework and in-silico validation of turbulent flow spectrum imaging (TFSI) of stenotic flow using phase-contrast MRI, evaluate systematic errors in quantitative turbulence parameter estimation, and propose a novel method for probing the Lagrangian velocity spectra of turbulent flows. THEORY AND METHODS The spectral response of velocity-encoding gradients is derived theoretically and linked to turbulence parameter estimation including the velocity autocorrelation function spectrum. Using a phase-contrast MRI simulation framework, the encoding properties of bipolar gradient waveforms with identical first gradient moments but different duration are investigated on turbulent flow data of defined characteristics as derived from computational fluid dynamics. Based on theoretical insights, an approach using velocity-compensated gradient waveforms is proposed to specifically probe desired ranges of the velocity autocorrelation function spectrum with increased accuracy. RESULTS Practical velocity-encoding gradients exhibit limited encoding power of typical turbulent flow spectra, resulting in up to 50% systematic underestimation of intravoxel SD values. Depending on the turbulence level in fluids, the error due to a single encoding gradient spectral response can vary by 20%. When using tailored velocity-compensated gradients, improved quantification of the Lagrangian velocity spectrum on a voxel-by-voxel basis is achieved and used for quantitative correction of intravoxel SD values estimated with velocity-encoding gradients. CONCLUSION To address systematic underestimation of turbulence parameters using bipolar velocity-encoding gradients in phase-contrast MRI of stenotic flows with short correlation times, tailored velocity-compensated gradients are proposed to improve quantitative mapping of turbulent blood flow characteristics.
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Affiliation(s)
- Hannes Dillinger
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Charles McGrath
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Christian Guenthner
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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6
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De Marinis D, Obrist D. Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations. Front Cardiovasc Med 2021; 8:742110. [PMID: 34796213 PMCID: PMC8594566 DOI: 10.3389/fcvm.2021.742110] [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: 07/15/2021] [Accepted: 10/06/2021] [Indexed: 11/13/2022] Open
Abstract
We propose a data assimilation methodology that can be used to enhance the spatial and temporal resolution of voxel-based data as it may be obtained from biomedical imaging modalities. It can be used to improve the assessment of turbulent blood flow in large vessels by combining observed data with a computational fluid dynamics solver. The methodology is based on a Stochastic Ensemble Kalman Filter (SEnKF) approach and geared toward pulsatile and turbulent flow configurations. We describe the observed flow fields by a mean value and its covariance. These flow fields are combined with forecasts obtained from a direct numerical simulation of the flow field. The method is validated against canonical pulsatile and turbulent flows. Finally, it is applied to a clinically relevant configuration, namely the flow downstream of a bioprosthetic valve in an aorta phantom. It is demonstrated how the 4D flow field obtained from experimental observations can be enhanced by the data assimilation algorithm. Results show that the presented method is promising for future use with in vivo data from 4D Flow Magnetic Resonance Imaging (4D Flow MRI). 4D Flow MRI returns spatially and temporally averaged flow fields that are limited by the spatial and the temporal resolution of the tool. These averaged flow fields and the associated uncertainty might be used as observation data in the context of the proposed methodology.
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Affiliation(s)
- Dario De Marinis
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Dipartimento di Meccanica, Matematica e Management and Centro di Eccellenza in Meccanica Computazionale, Politecnico di Bari, Bari, Italy
| | - Dominik Obrist
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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7
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Zhuang B, Sirajuddin A, Zhao S, Lu M. The role of 4D flow MRI for clinical applications in cardiovascular disease: current status and future perspectives. Quant Imaging Med Surg 2021; 11:4193-4210. [PMID: 34476199 DOI: 10.21037/qims-20-1234] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 04/23/2021] [Indexed: 11/06/2022]
Abstract
Magnetic resonance imaging (MRI) four-dimensional (4D) flow is a type of phase-contrast (PC) MRI that uses blood flow encoded in 3 directions, which is resolved relative to 3 spatial and temporal dimensions of cardiac circulation. It can be used to simultaneously quantify and visualize hemodynamics or morphology disorders. 4D flow MRI is more comprehensive and accurate than two-dimensional (2D) PC MRI and echocardiography. 4D flow MRI provides numerous hemodynamic parameters that are not limited to the basic 2D parameters, including wall shear stress (WSS), pulse wave velocity (PWV), kinetic energy, turbulent kinetic energy (TKE), pressure gradient, and flow component analysis. 4D flow MRI is widely used to image many parts of the body, such as the neck, brain, and liver, and has a wide application spectrum to cardiac diseases and large vessels. This present review aims to summarize the hemodynamic parameters of 4D flow MRI technology and generalize their usefulness in clinical practice in relation to the cardiovascular system. In addition, we note the improvements that have been made to 4D flow MRI with the application of new technologies. The application of new technologies can improve the speed of 4D flow, which would benefit clinical applications.
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Affiliation(s)
- Baiyan Zhuang
- Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Arlene Sirajuddin
- National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Shihua Zhao
- Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Minjie Lu
- Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Cardiovascular Imaging (Cultivation), Chinese Academy of Medical Sciences, Beijing, China
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8
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Nolte D, Urbina J, Sotelo J, Sok L, Montalba C, Valverde I, Osses A, Uribe S, Bertoglio C. Validation of 4D Flow based relative pressure maps in aortic flows. Med Image Anal 2021; 74:102195. [PMID: 34419837 DOI: 10.1016/j.media.2021.102195] [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: 10/29/2020] [Revised: 06/11/2021] [Accepted: 07/22/2021] [Indexed: 12/18/2022]
Abstract
While the clinical gold standard for pressure difference measurements is invasive catheterization, 4D Flow MRI is a promising tool for enabling a non-invasive quantification, by linking highly spatially resolved velocity measurements with pressure differences via the incompressible Navier-Stokes equations. In this work we provide a validation and comparison with phantom and clinical patient data of pressure difference maps estimators. We compare the classical Pressure Poisson Estimator (PPE) and the new Stokes Estimator (STE) against catheter pressure measurements under a variety of stenosis severities and flow intensities. Specifically, we use several 4D Flow data sets of realistic aortic phantoms with different anatomic and hemodynamic severities and two patients with aortic coarctation. The phantom data sets are enriched by subsampling to lower resolutions, modification of the segmentation and addition of synthetic noise, in order to study the sensitivity of the pressure difference estimators to these factors. Overall, the STE method yields more accurate results than the PPE method compared to catheterization data. The superiority of the STE becomes more evident at increasing Reynolds numbers with a better capacity of capturing pressure gradients in strongly convective flow regimes. The results indicate an improved robustness of the STE method with respect to variation in lumen segmentation. However, with heuristic removal of the wall-voxels, the PPE can reach a comparable accuracy for lower Reynolds' numbers.
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Affiliation(s)
- David Nolte
- Bernoulli Institute, University of Groningen, Groningen, 9747AG, The Netherlands; Center for Mathematical Modeling, Universidad de Chile, Santiago, 8370456, Chile
| | - Jesús Urbina
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, 7820436, Chile; Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, 833002, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, 7820436, Chile
| | - Julio Sotelo
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, 7820436, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, 7820436, Chile; School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile; Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, 7820436, Chile
| | - Leo Sok
- Bernoulli Institute, University of Groningen, Groningen, 9747AG, The Netherlands
| | - Cristian Montalba
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, 7820436, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, 7820436, Chile
| | - Israel Valverde
- Hospital Universitario Virgen del Rocío, Sevilla, 41013, Spain
| | - Axel Osses
- Center for Mathematical Modeling, Universidad de Chile, Santiago, 8370456, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, 7820436, Chile
| | - Sergio Uribe
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, 7820436, Chile; Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, 833002, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, 7820436, Chile
| | - Cristóbal Bertoglio
- Bernoulli Institute, University of Groningen, Groningen, 9747AG, The Netherlands; Center for Mathematical Modeling, Universidad de Chile, Santiago, 8370456, Chile.
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9
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Corso P, Walheim J, Dillinger H, Giannakopoulos G, Gülan U, Frouzakis CE, Kozerke S, Holzner M. Toward an accurate estimation of wall shear stress from 4D flow magnetic resonance downstream of a severe stenosis. Magn Reson Med 2021; 86:1531-1543. [PMID: 33914962 DOI: 10.1002/mrm.28795] [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: 09/15/2020] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE First, to investigate the agreement between velocity, velocity gradient, and Reynolds stress obtained from four-dimensional flow magnetic resonance (4D flow MRI) measurements and direct numerical simulation (DNS). Second, to propose and optimize based on DNS, 2 alternative methods for the accurate estimation of wall shear stress (WSS) when the resolution of the flow measurements is limited. Thirdly, to validate the 2 methods based on 4D flow MRI data. METHODS In vitro 4D MRI has been conducted in a realistic rigid stenosed aorta model under a constant flow rate of 12 L/min. A DNS of transitional stenotic flow has been performed using the same geometry and boundary conditions. RESULTS Time-averaged velocity and Reynolds stresses are in good agreement between in vitro 4D MRI data and DNS (errors between 2% and 8% of the reference downsampled data). WSS estimation based on the 2 proposed methods applied to MRI data provide good agreement with DNS for slice-averaged values (maximum error is less than 15% of the mean reference WSS for the first method and 25% for the second method). The performance of both models is not strongly sensitive to spatial resolution up to 1.5 mm voxel size. While the performance of model 1 deteriorates appreciably at low signal-to-noise ratios, model 2 remains robust. CONCLUSIONS The 2 methods for WSS magnitude give an overall better agreement than the standard approach used in the literature based on direct calculation of the velocity gradient close to the wall (relative error of 84%).
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Affiliation(s)
- Pascal Corso
- Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland.,ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Jonas Walheim
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Hannes Dillinger
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - George Giannakopoulos
- Aerothermochemistry and Combustion Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | | | | | - Sebastian Kozerke
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Markus Holzner
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zurich, Switzerland.,Swiss Federal Institute of Aquatic Science and Technology Eawag, Zurich, Switzerland
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Corso P, Giannakopoulos G, Gulan U, Frouzakis CE, Holzner M. A Novel Estimation Approach of Pressure Gradient and Haemodynamic Stresses as Indicators of Pathological Aortic Flow Using Subvoxel Modelling. IEEE Trans Biomed Eng 2020; 68:980-991. [PMID: 32816672 DOI: 10.1109/tbme.2020.3018173] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The flow downstream from aortic stenoses is characterised by the onset of shear-induced turbulence that leads to irreversible pressure losses. These extra losses represent an increased resistance that impacts cardiac efficiency. A novel approach is suggested in this study to accurately evaluate the pressure gradient profile along the aorta centreline using modelling of haemodynamic stress at scales that are smaller than the typical resolution achieved in experiments. METHODS We use benchmark data obtained from direct numerical simulation (DNS) along with results from in silico and in vitro three-dimensional particle tracking velocimetry (3D-PTV) at three voxel sizes, namely 750 μm, 1 mm and 1.5 mm. A differential equation is derived for the pressure gradient, and the subvoxel-scale (SVS) stresses are closed using the Smagorinsky and a new refined model. Model constants are optimised using DNS and in silico PTV data and validated based on pulsatile in vitro 3D-PTV data and pressure catheter measurements. RESULTS The Smagorinsky-based model was found to be more accurate for SVS stress estimation but also more sensitive to errors especially at lower resolution, whereas the new model was found to more accurately estimate the projected pressure gradient even for larger voxel size of 1.5 mm albeit at the cost of increased sensitivity at this voxel size. A comparison with other methods in the literature shows that the new approach applied to in vitro PTV measurements estimates the irreversible pressure drop by decreasing the errors by at least 20%. CONCLUSION Our novel approach based on the modelling of subvoxel stress offers a validated and more accurate way to estimate pressure gradient, irreversible pressure loss and SVS stress. SIGNIFICANCE We anticipate that the approach may potentially be applied to image-based in vivo, in vitro 4D flow data or in silico data with limited spatial resolution to assess pressure loss and SVS stresses in disturbed aortic blood flow.
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