1st supervisor: Pablo Lamata, King’s College London
2nd supervisor: Mengxing Tang, Imperial College London
Background: the clinical need
The pressure drop is the key functional metric to assess the severity of flow constrictions such as those caused by aortic stenosis (AS) , hypertrophic obstructive cardiomyopathy (HOCM)  or aortic coarctation (AoC)  – Fig.1. The pressure drop is critical to define the optimal timing for clinical (drugs) or surgical (i.e. valve replacement, myectomy, stent…) therapies.
Invasive catheterised pressure sensors (Fig.2) are considered the most accurate method, but their actual application is very limited due to associated costs and risks (i.e. infant population of congenital conditions with a 5% of severe events ). The non-invasive alternative is based on echocardiographic Doppler data , , but its accuracy and precision is reduced by its heavy simplifications of the flow physics , and its application is restricted to the availability of acoustic windows (i.e. no rigid opaque structures and depth that can be reached) with the correct probe orientation (i.e. parallel to the direction of flow).
There is thus a need for accurate, robust and non-invasive methods to assess the severity of flow obstructions.
PhD hypothesis and objective
The hypothesis to solve the clinical need is the combination of recent advances in echocardiography (ultrafast acquisition enabling the assessment of blood velocity) and computational flow dynamics (application of Newton’s fundamental laws to make an optimal estimation of the pressure drop with the minimum information). The objective is thus to develop the envisioned solution, designing (1) optimal blood flow velocity acquisitions with ultrasound strategies (particle tracking and Doppler effect as the main ones) and pressure mapping computational techniques (i.e. the use of virtual fields). The aim is to propose an improved assessment of pressure biomarkers for the risk stratification of flow constrictions.
The project draws a synergy between two areas of research, on the acquisition and on the analysis of medical images. Imperial and KCL are at the forefront on these areas, providing the technology to capture velocity profiles with echocardiography [6,7] and resolving the physics of the flow to derive the required pressure drop estimations to resolve the clinical diagnostic and prognostic questions .
 Nishimura et al “2014 AHA/ACC guideline for the management of patients with valvular heart disease,” J. Am. Coll. Cardiol., vol. 63, no. 22, pp. e57-185, Jun. 2014.
 Elliott et al “2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy,” Eur. Heart J., vol. 35, no. 39, pp. 2733–2779, Oct. 2014.
 Baumgartner et al “ESC Guidelines for the management of grown-up congenital heart disease” Eur. Heart J., vol. 31, no. 23, pp. 2915–2957, Dec. 2010.
 Bergersen et al “Catheterization for Congenital Heart Disease Adjustment for Risk Method (CHARM),” JACC Cardiovasc. Interv., vol. 4, no. 9, pp. 1037–1046, Sep. 2011.
 Donati et al “Beyond Bernoulli: Improving the Accuracy and Precision of Noninvasive Estimation of Peak Pressure Drops.,” Circ. Cardiovasc. Imaging, vol. 10, no. 1, p. e005207-, Jan. 2017.
 Zhou et al “3-D Velocity and Volume Flow Measurement In-Vivo Using Speckle Decorrelation and 2-D High-Frame-Rate Contrast-Enhanced Ultrasound” IEEE T Ultrasonics, 65:12 ,Dec. 2018
 Matthieu et al, High Frame-Rate Contrast Echocardiography: In-Human Demonstration; JACC: Cardiovascular Imaging, Dec. 2017