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  • 1.
    Dyverfeldt, Petter
    et al.
    University of California, San Francisco, USA.
    Hope, Michael D.
    University of California, San Francisco, USA.
    Tseng, Elaine E.
    University of California, San Francisco, USA.
    Saloner, David
    University of California, San Francisco, USA.
    Magnetic Resonance Measurement of Turbulent Kinetic Energy for the Estimation of Irreversible Pressure Loss in Aortic Stenosis2013In: JACC Cardiovascular Imaging, ISSN 1936-878X, E-ISSN 1876-7591, Vol. 6, no 1, p. 64-71Article in journal (Refereed)
    Abstract [en]

    Objectives

    The authors sought to measure the turbulent kinetic energy (TKE) in the ascending aorta of patients with aortic stenosis and to assess its relationship to irreversible pressure loss.

    Background

    Irreversible pressure loss caused by energy dissipation in post-stenotic flow is an important determinant of the hemodynamic significance of aortic stenosis. The simplified Bernoulli equation used to estimate pressure gradients often misclassifies the ventricular overload caused by aortic stenosis. The current gold standard for estimation of irreversible pressure loss is catheterization, but this method is rarely used due to its invasiveness. Post-stenotic pressure loss is largely caused by dissipation of turbulent kinetic energy into heat. Recent developments in magnetic resonance flow imaging permit noninvasive estimation of TKE.

    Methods

    The study was approved by the local ethics review board and all subjects gave written informed consent. Three-dimensional cine magnetic resonance flow imaging was used to measure TKE in 18 subjects (4 normal volunteers, 14 patients with aortic stenosis with and without dilation). For each subject, the peak total TKE in the ascending aorta was compared with a pressure loss index. The pressure loss index was based on a previously validated theory relating pressure loss to measures obtainable by echocardiography.

    Results

    The total TKE did not appear to be related to global flow patterns visualized based on magnetic resonance–measured velocity fields. The TKE was significantly higher in patients with aortic stenosis than in normal volunteers (p < 0.001). The peak total TKE in the ascending aorta was strongly correlated to index pressure loss (R2 = 0.91).

    Conclusions

    Peak total TKE in the ascending aorta correlated strongly with irreversible pressure loss estimated by a well-established method. Direct measurement of TKE by magnetic resonance flow imaging may, with further validation, be used to estimate irreversible pressure loss in aortic stenosis.

  • 2.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry2019In: JACC Cardiovascular Imaging, ISSN 1936-878X, E-ISSN 1876-7591Article in journal (Refereed)
  • 3.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry2020In: JACC Cardiovascular Imaging, ISSN 1936-878X, E-ISSN 1876-7591, Vol. 13, no 3, p. 760-770Article in journal (Refereed)
    Abstract [en]

    Objectives: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning–based coronary computed tomography (CT) angiography (cCTA)–derived fractional flow reserve (CT-FFR). Background: CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated. Methods: A total of 482 vessels from 314 patients (age 62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard. Results: The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001). Conclusions: Machine-learning–based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621)

  • 4.
    Sengupta, Partho P
    et al.
    Mt Sinai School of Medicine.
    Pedrizzetti, Gianni
    University of Trieste.
    Kilner, Philip J
    Royal Brompton Hospital.
    Kheradvar, Arash
    University of Calif Irvine.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology.
    Tonti, Giovanni
    SS Annunziata Hospital.
    Fraser, Alan G
    Cardiff University.
    Narula, Jagat
    Mt Sinai School of Medicine.
    Emerging Trends in CV Flow Visualization2012In: JACC Cardiovascular Imaging, ISSN 1936-878X, E-ISSN 1876-7591, Vol. 5, no 3, p. 305-316Article in journal (Refereed)
    Abstract [en]

    Blood flow patterns are closely linked to the morphology and function of the cardiovascular system. These patterns reflect the exceptional adaptability of the cardiovascular system to maintain normal blood circulation under a wide range of workloads. Accurate retrieval and display of flow-related information remains a challenge because of the processes involved in mapping the flow velocity fields within specific chambers of the heart. We review the potentials and pitfalls of current approaches for blood flow visualization, with an emphasis on acquisition, display, and analysis of multidirectional flow. This document is divided into 3 sections. First, we provide a descriptive outline of the relevant concepts in cardiac fluid mechanics, including the emergence of rotation in flow and the variables that delineate vortical structures. Second, we elaborate on the main methods developed to image and visualize multidirectional cardiovascular flow, which are mainly based on cardiac magnetic resonance, ultrasound Doppler, and contrast particle imaging velocimetry, with recommendations for developing dedicated imaging protocols. Finally, we discuss the potential clinical applications and technical challenges with suggestions for further investigations.

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