Figure - available from: Frontiers in Cardiovascular Medicine
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Representative flow waveform measurement for one patient. (a,b) Magnitude and phase images acquired from PCMR acquisition with the coronary sinus outlined. (c) Both the basal and hyperemic flows were acquired using PCMR, and the basal flow was then scaled by a range of CFR values. When the basal flow is scaled by the patient-specific CFR—2.8—, the time-average flow rate is the same for both it and the hyperemic flow. Scaling the basal flow by the cohort-average CFR—2.2—produces the same basal waveform but with a different time-average flow rate from the hyperemic flow.
Source publication
The purpose of this study is to investigate the effect of varying coronary flow reserve (CFR) values on the calculation of computationally-derived fractional flow reserve (FFR). CFR reflects both vessel resistance due to an epicardial stenosis, and resistance in the distal microvascular tissue. Patients may have a wide range of CFR related to the t...
Citations
... Therefore, a total of 67 independent populations were included. Among the 47 studies, 22 were from Japan [19-21, 23-37, 42, 44, 45, 47], eight from the United States [7,14,22,40,41,48,51,55], five from Sweden [12,16,17,53,54], three from Germany and Sweden [11,15,46], two from Finland [38,39], and one each from Australia [18], France [43], Italy [9], Norway [13], Turkey [10], the United Kingdom [49], Switzerland [7], and the Netherlands [50]. Year of publication ranged from 1992 to 2022. ...
... Year of publication ranged from 1992 to 2022. In 35 studies, 1.5T CMR systems were used [7, 9-12, 15-17, 19-21, 24-32, 34-40, 42, 46-49, 51, 52, 54]; in nine studies, 3T CMR systems were used [13,14,18,22,33,41,43,53,55]; in one study, both 1.5 and 3T CMR units were used [44]; and in one study, a 0.6T CMR system was used [50]. Information on pharmacological stress was extracted from 41 studies. ...
... According to Moro et al. [43], the VENC was 150 cm/s. Thirteen studies performed phase-offset correction using adjacent myocardial tissue [21, 28-31, 33, 35-39, 47, 55], and one study used static tissue regions in the chest wall [55]. The standard imaging parameters and analytical images related to phase-contrast cine CMR are presented (Additional files 1, 2). ...
Background
Phase-contrast cine cardiovascular magnetic resonance (CMR) of the coronary sinus has emerged as a non-invasive method for measuring coronary sinus blood flow and coronary flow reserve (CFR). However, its clinical utility has not yet been established. Here we performed a meta-analysis to clarify the clinical value of CMR-derived CFR in various cardiovascular diseases.
Methods
An electronic database search was performed of PubMed, Web of Science Core Collection, Cochrane Advanced Search, and EMBASE. We compared the CMR-derived CFR of various cardiovascular diseases (stable coronary artery disease [CAD], hypertrophic cardiomyopathy [HCM], dilated cardiomyopathy [DCM]) and control subjects. We assessed the prognostic value of CMR-derived CFR for predicting major adverse cardiac events (MACE) in patients with stable CAD.
Results
A total of 47 eligible studies were identified. The pooled CFR from our meta-analysis was 3.48 (95% confidence interval [CI], 2.98–3.98) in control subjects, 2.50 (95% CI, 2.38–2.61) in stable CAD, 2.01 (95% CI, 1.70–2.32) in cardiomyopathies (HCM and DCM). The meta-analysis showed that CFR was significantly reduced in stable CAD (mean difference [MD] = −1.48; 95% CI, −1.78 to −1.17; p < 0.001; I ² = 0%; p for heterogeneity = 0.33), HCM (MD = −1.20; 95% CI, −1.63 to −0.77; p < 0.001; I ² = 0%; p for heterogeneity = 0.49), and DCM (MD = −1.53; 95% CI, −1.93 to −1.13; p < 0.001; I ² = 0%; p for heterogeneity = 0.45). CMR-derived CFR was an independent predictor of MACE for patients with stable CAD (hazard ratio = 0.52 per unit increase; 95% CI, 0.37–0.73; p < 0.001; I ² = 84%, p for heterogeneity < 0.001).
Conclusions
CMR-derived CFR was significantly decreased in cardiovascular diseases, and a decreased CFR was associated with a higher occurrence of MACE in patients with stable CAD. These results suggest that CMR-derived CFR has potential for the pathological evaluation of stable CAD, cardiomyopathy, and risk stratification in CAD.
... Moreover, phase-contrast magnetic resonance imaging (PC-MRI) allowed coronary flow waveforms determination under rest and stress conditions [77], while self-gating principles improved vessel recognition by correcting for physiologic motion [78]. The obtained patient-specific coronary flow values were applied as inflow BCs to determine FFR based on CFD simulations [79]. This technology is currently undergoing further clinical investigation. ...
Hemodynamics interacts with the cellular components of human vessels, influencing function and healthy status. Locally acting hemodynamic forces have been associated—by a steadily increasing amount of scientific evidence—with nucleation and evolution of atherosclerotic plaques in several vascular regions, resulting in the formulation of the ‘hemodynamic risk hypothesis’ of the atherogenesis. At the level of coronary arteries, however, the complexity of both anatomy and physiology made the study of this vascular region particularly difficult for researchers. Developments in computational fluid dynamics (CFD) have recently allowed an accurate modelling of the intracoronary hemodynamics, thus offering physicians a unique tool for the investigation of this crucial human system by means of advanced mathematical simulations. The present review of CFD applications in coronary artery disease was set to concisely offer the medical reader the theoretical foundations of quantitative intravascular hemodynamics—reasoned schematically in the text in its basic (i.e., pressure and velocity) and derived quantities (e.g., fractional flow reserve, wall shear stress and helicity)—along with its current implications in clinical research. Moreover, attention was paid in classifying computational modelling derived from invasive and non-invasive imaging modalities with unbiased remarks on the advantages and limitations of each procedure. Finally, an extensive description—aided by explanatory figures and cross references to recent clinical findings—was presented on the role of near-wall hemodynamics, in terms of shear stress, and of intravascular flow complexity, in terms of helical flow.
Murray's law has been viewed as a fundamental law of physiology. Relating blood flow (Q) to vessel diameter (D) (Q µD ³ ), it dictates minimum lumen area (MLA) targets for coronary bifurcation percutaneous coronary intervention (PCI). The cubic exponent (3.0) however, has long been disputed, with alternative theoretical derivations arguing this should be closer to 2.33 (7/3). The aim of this meta-analysis was to quantify the optimum flow-diameter exponent in human and mammalian coronary arteries. We conducted a systematic review and meta-analysis of all articles quantifying an optimum flow-diameter exponent for mammalian coronary arteries within the Cochrane library, PubMed Medline, Scopus, and Embase databases on 20/3/2023. A random-effects meta-analysis was used to determine a pooled flow-diameter exponent. Risk of bias was assessed with the National Institutes of Health (NIH) quality assessment tool, funnel plots and Egger regression. From a total of 4,524 articles, 18 were suitable for meta-analysis. Studies included data from 1,070 unique coronary trees, taken from 372 humans and 112 animals. The pooled flow diameter exponent across both epicardial and transmural arteries was 2.39 (95% CI 2.24 - 2.54, I ² = 99%). The pooled exponent of 2.39 showed very close agreement with the theoretical exponent of 2.33 (7/3) reported by Kassab and colleagues. This exponent may provide a more accurate description of coronary morphometric scaling in human and mammalian coronary arteries, as compared with Murray's original law. This has important implications for the assessment, diagnosis, and interventional treatment of coronary artery disease.
Machine learning (ML) and deep learning (DL) techniques have been increasingly applied to help diagnose coronary artery disease (CAD) as well as help with patient management decisions. Imaging has begun to play a larger role in these studies. Cardiovascular magnetic resonance (CMR) offers multiple techniques to diagnose CAD, and ML and DL have been used with these techniques in an effort to improve both the image quality and the speed of image interpretation. In particular, ML and DL have been applied to direct imaging of coronary vessel anatomy, imaging of coronary flow, and myocardial perfusion imaging. In applications aimed at imaging the coronary artery anatomy, ML and DL techniques have been used to improve image quality in reconstruction, improve the speed of reconstruction, allow for more sparse sampling of data, and enable automated evaluation of image quality. In applications of coronary flow imaging, ML and DL techniques have been used to reduce the uncertainty of phase-contrast measurements of blood velocity and flow, and physics-informed neural networks have been used to improve the modeling of flow based on both acquired image data and natural laws of motion. In myocardial perfusion imaging, ML and DL techniques have been used at multiple steps in the image analysis process to automate quantitative blood flow measurements, including motion correction, image registration, tracer kinetic modeling, and detection of perfusion defects. Future applications of ML and DL in evaluating CAD are expected to continue to develop with increasing impact in both diagnosis and patient management.
