Noninvasive Estimation of the Pressure Gradient Across Stenoses Using Sonographic Contrast

Lien, W.W.; Lee, Allen H.; Kono, Yuko; Steinbach, G.; Mattrey, Robert F.

doi:10.7863/jum.2004.23.5.683

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…In the past, researchers have been using a series of simplified equations, either empirical or derived from fluid mechanics theory, for the non-invasive clinical estimation of the pressure gradient in cardiovascular stenosis (including in heart valves, [ 4 1 _ T D $ D I F F ] atherosclerosis, and AoCo). In these simplified models, flow velocity and the geometry of the flow channel in the stenosis were estimated from either Doppler echocardiography [10][11][12][13][14][15][16][17][18][19][20] or magnetic resonance imaging (MRI) [21][22][23] measurements. This non-invasive procedure seemed to achieve fair agreement in the diagnosis of stenosis in heart valves and [ 4 1 _ T D $ D I F F ] atherosclerosis, and has been extended to analyze other vascular diseases [24,25].…”

Section: Introductionmentioning

confidence: 99%

Patient-specific non-invasive estimation of pressure gradient across aortic coarctation using magnetic resonance imaging

Shi

Valverde

Lawford

et al. 2019

Journal of Cardiology

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Background: Non-invasive estimation of the pressure gradient in aortic coarctation has much clinical importance in assisting the diagnosis and treatment of the disease. Previous researchers applied computational fluid dynamics for the prediction of the pressure gradient in aortic coarctation. The accuracy of the prediction was satisfactory but the procedure was time-consuming and resourcedemanding. Method: In this research a magnetic resonance imaging (MRI)-based non-invasive modeling procedure is implemented to predict the pressure gradient in 14 patient cases of aortic coarctation. Multi-cycle patient flow and pressure data are processed to produce the flow and pressure conditions in the patient cases. Bernoulli equation-based friction loss model combined with the inertial effect of the blood flow in the vessel segments are applied to model the pressure gradient in the aortic coarctation. The modelpredicted pressure gradient data are then compared with the catheter in vivo measurement data for validation. Results:The MRI-based model prediction technique produces results that are consistent with those from the catheter measurement, based on the criteria of both the cycle-averaged instantaneous pressure gradient and the peak-to-peak pressure gradient. Conclusion:This study suggests that the MRI-based non-invasive modeling procedure has much potential to be applied in clinical practice for the prediction of the pressure gradient in aortic coarctation patients.

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