Pressure drop mapping using 4D flow MRI in patients with bicuspid aortic valve disease: A novel marker of valvular obstruction

Hassanabad, Ali Fatehi; Burns, Fiona; Bristow, Mike; Lydell, Carmen; Howarth, Andrew G; Heydari, Bobak; Gao, Xuexin; Fedak, Paul W.M.; White, James A.; García, Julio

doi:10.1016/j.mri.2019.11.011

Cited by 32 publications

(24 citation statements)

References 33 publications

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“…PD measurements have demonstrated clinical feasibility [ 9 ] and are calculated by a previously validated extended Bernoulli equation – which demonstrates accurate inclusion of the pressure recovery phenomenon. [ 9 , 10 ] PD was analyzed at each analysis plane and reported as a peak systolic deviance to the pressure value at LVOT (peak systolic pressure drop [PD sys ]; mm Hg).…”

Section: Methodsmentioning

confidence: 99%

Bicuspid aortic valve disease is associated with abnormal wall shear stress, viscous energy loss, and pressure drop within the ascending thoracic aorta

et al. 2021

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Bicuspid aortic valve (BAV) disease has significant gaps in its clinical management practices. To highlight the potential utility of advanced hemodynamic biomarkers in strengthening BAV assessment, we used 4-dimentional flow magnetic resonance imaging to investigate altered hemodynamics in the ascending aorta (AAo). A total of 32 healthy controls and 53 age-matched BAV patients underwent cardiac magnetic resonance imaging at 3T, with cine imaging and 4D-flow. Analysis planes were placed along 3D-segmented aortas at the left ventricular outflow tract (LVOT), sinuses of Valsalva, mid-ascending aorta (MAA), and proximal to the first aortic branch. Locations were analyzed for aortic diameter (normalized to body surface area), pressure drop (PD), viscous energy loss (EL), and wall shear stress (WSS) sub-vectors (axial wall shear stress, circumferential wall shear stress [WSS C ], magnitude wall shear stress). Student's t tests, or non-parametric equivalents, compared parameters between cohorts. Univariable and multivariable analyses explored the associations of AAo diameter with hemodynamics within the BAV cohort. Compared to control cohort, BAV patients showed significantly greater PD (MAA: 9.5 ± 8.0 vs 2.8 ± 2.4 mm Hg; P < .01), EL (from LVOT-AA1: 7.39 ± 4.57 mW vs 2.90 ± 1.07 mW; P < .01), and WSS C (MAA: 0.3 ± 0.1 vs 0.2 ± 0.06 Pa; P ≤ .01) throughout the AAo. Correlational analyses revealed an inverse association between AAo diameter and both magnitude wall shear stress and axial wall shear stress. BAV patients exhibited increased PD, EL, and WSS C in the AAo, and an inverse association between AAo diameter and WSS sub-vectors. This demonstrated the impact of PD, EL, and WSS in BAV disease and the importance of altered hemodynamics in aortic remodelling.

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Section: Methodsmentioning

confidence: 99%

Bicuspid aortic valve disease is associated with abnormal wall shear stress, viscous energy loss, and pressure drop within the ascending thoracic aorta

et al. 2021

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“…1 4 D flow MRI analysis was performed using a prototype module from cvi42. 11,12 Pre-processing corrections were applied to reduce noise, correct for eddy currents, and perform phase unwrapping in the case of velocity aliasing. ML module was trained for LV segmentation and valve location identification using publicly cardiac MRI data from the UK biobank.…”

Section: Patientsmentioning

confidence: 99%

Aortic and mitral flow quantification using dynamic valve tracking and machine learning: Prospective study assessing static and dynamic plane repeatability, variability and agreement

García

Beckie

Hassanabad

et al. 2021

JRSM Cardiovascular Disease

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Background Blood flow is a crucial measurement in the assessment of heart valve disease. Time-resolved flow using magnetic resonance imaging (4 D flow MRI) can provide a comprehensive assessment of heart valve hemodynamics but it relies in manual plane analysis. In this study, we aimed to demonstrate the feasibility of automate the detection and tracking of aortic and mitral valve planes to assess blood flow from 4 D flow MRI. Methods In this prospective study, a total of n = 106 subjects were enrolled: 19 patients with mitral disease, 65 aortic disease patients and 22 healthy controls. Machine learning was employed to detect aortic and mitral location and motion in a cine three-chamber plane and a perpendicular projection was co-registered to the 4 D flow MRI dataset to quantify flow volume, regurgitant fraction, and a peak velocity. Static and dynamic plane association and agreement were evaluated. Intra- and inter-observer, and scan-rescan reproducibility were also assessed. Results Aortic regurgitant fraction was elevated in aortic valve disease patients as compared with controls and mitral valve disease patients ( p < 0.05). Similarly, mitral regurgitant fraction was higher in mitral valve patients ( p < 0.05). Both aortic and mitral total flow were high in aortic patients. Static and dynamic were good (r > 0.6, p < 0.005) for aortic total flow and peak velocity, and mitral peak velocity and regurgitant fraction. All measurements showed good inter- and intra-observer, and scan-rescan reproducibility. Conclusion We demonstrated that aortic and mitral hemodynamics can efficiently be quantified from 4 D flow MRI using assisted valve detection with machine learning.

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“…14,28,36,46 In recent years, 4D flow magnetic resonance imaging (MRI) has been increasingly used to obtain information on both aortic morphology and hemodynamics 5,14,31,35,43 -in particular in the presence of aortic vascular/valve pathologies 4,16,23,31 -providing risk markers of AAo wall degeneration. 14,25,43 With the objective of providing a comprehensive characterization of the spatiotemporal heterogeneity of large-scale aortic flow features and of their possible links with AAo dilation, a recently proposed approach integrating computational hemodynamics with Complex Networks (CNs) theory 7,8,13,37 is here extended for the first time to 4D flow MRI in patients with and without aortic dilation. In this study we explored the capability of CNs to characterize in vivo the dynamics of dominant aortic flow features using the time-histories of the measured velocity data along the cardiac cycle.…”

Section: Introductionmentioning

confidence: 99%

Combining 4D Flow MRI and Complex Networks Theory to Characterize the Hemodynamic Heterogeneity in Dilated and Non-dilated Human Ascending Aortas

et al. 2021

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Motivated by the evidence that the onset and progression of the aneurysm of the ascending aorta (AAo) is intertwined with an adverse hemodynamic environment, the present study characterized in vivo the hemodynamic spatiotemporal complexity and organization in human aortas, with and without dilated AAo, exploring the relations with clinically relevant hemodynamic and geometric parameters. The Complex Networks (CNs) theory was applied for the first time to 4D flow magnetic resonance imaging (MRI) velocity data of ten patients, five of them presenting with AAo dilation. The time-histories along the cardiac cycle of velocity-based quantities were used to build correlation-based CNs. The CNs approach succeeded in capturing large-scale coherent flow features, delimiting flow separation and recirculation regions. CNs metrics highlighted that an increasing AAo dilation (expressed in terms of the ratio between the maximum AAo and aortic root diameter) disrupts the correlation in forward flow reducing the correlation persistence length, while preserving the spatiotemporal homogeneity of secondary flows. The application of CNs to in vivo 4D MRI data holds promise for a mechanistic understanding of the spatiotemporal complexity and organization of aortic flows, opening possibilities for the integration of in vivo quantitative hemodynamic information into risk stratification and classification criteria.

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Pressure drop mapping using 4D flow MRI in patients with bicuspid aortic valve disease: A novel marker of valvular obstruction

Cited by 32 publications

References 33 publications

Bicuspid aortic valve disease is associated with abnormal wall shear stress, viscous energy loss, and pressure drop within the ascending thoracic aorta

Bicuspid aortic valve disease is associated with abnormal wall shear stress, viscous energy loss, and pressure drop within the ascending thoracic aorta

Aortic and mitral flow quantification using dynamic valve tracking and machine learning: Prospective study assessing static and dynamic plane repeatability, variability and agreement

Combining 4D Flow MRI and Complex Networks Theory to Characterize the Hemodynamic Heterogeneity in Dilated and Non-dilated Human Ascending Aortas

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