Magnetic Resonance Elastography (MRE) (1) is a powerful diagnostic imaging technique that measures changes in the biomechanical properties of biological tissue caused by disease. MRE works by delivering mechanical waves to the tissue, which are measured using MRI, and these wave measurements are converted into estimated biomechanical properties using specialised algorithms. MRE research has recently begun at the Sir Peter Mansfield Imaging Centre (SPMIC), University of Nottingham, with the installation of an MRE system on the Philips 3-Tesla Ingenia MRI scanner.
For the liver, MRE is used clinically to detect the changes in elasticity (or stiffness) and viscosity associated with diseases such as fibrosis and cirrhosis (2). However, it is highly sensitive to increases in blood pressure in the hepatic vein, and other (patient-specific) structural features (such as high-contrast changes in elasticity, as occurs between tissue and vasculature) that place significant uncertainty on the interpretation of MRE measures in a diagnostic setting. Hence, there is a pressing clinical need to improve understanding of the influences of liver biophysics on MRE results, in order to provide accurate, patient-specific diagnoses. The hepatic venous pressure gradient (HVPG) is an important measure for the assessment of liver health: it is associated with congestive heart failure and predictive of oesophageal varices (bleeding). Liver MRE is being evaluated as a means to measure HVPG (3), but this is confounded by the sensitivity of liver MRE to tissue structure alterations in conditions such as liver fibrosis and cirrhosis.
Computational simulation is already used to generate predictions of MRE data (4), but currently without the level of sophistication required to incorporate the details of the liver’s structure, mechanical properties and blood flow required to accurately predict their effects on MRE measurements in a precision medicine environment. In this project, personalised finite element liver models will be built from anatomical MRI data obtained from individual patients for whom MRE data are also measured. In addition the project will involve the development of 4D-flow MRI measurements to assess corresponding blood flow in the same participants. Multi-physics finite element simulations of MRE in these models will produce simulated MRE data for liver tissue, incorporating their dependence on material properties (such as elasticity, viscosity) and hepatic blood flow. The simulated MRE data will be compared with the measured liver MRE data to improve understanding of uncertainty in the interpretation of MRE results and their sensitivity to the underlying diseases.
Since determination of the influence of hepatic venous pressure in liver MRE remains an unsolved problem, with very little prior work, this project is highly novel and the findings will be very important to liver MRE and liver medicine in general. Moreover, the understanding gained on the influence of blood flow on MRE measurements in the liver could provide insight into the influence of blood flow on MRE in other sites of the body, such as the spleen, brain and muscle. Furthermore, the simulation platform and techniques developed for liver could be adapted for other sites in the body in future projects.
Initial investigations will entail simulation of MRE in a simplified model, wherein the influence of hepatic venous pressure is modelled parametrically. Subsequent work will seek understanding of the fully-coupled flow-structure interaction.
The student will be provided with an excellent training environment within the Centre for Mathematical Medicine and Biology (CMMB) and Schools of Medicine and Physics. Computational work will be expedited by a dedicated and cutting edge computing infrastructure, and access to HPC facilities; interdisciplinary supervision - with complementary expertise in scientific computing (MH) mathematical modelling (RO, BB) and MRE (DM), and frequent contact with researchers within the School of Medicine and the SPMIC in the School of Physics, will ensure that appropriate theoretical developments are tailored to, and incorporate, relevant biology and experimental developments.
Technical training encompasses both subject-specific and broader research activities, such as attendance at mathematical and biological conferences, workshops/sandpits, and CMMB seminars or journal clubs. Other activities include training in outreach, research and presentation techniques, and career development support via PD, graduate school. Around 30 credits will be completed (min. 10 assessed in Year 1). Specific activities will depend upon the needs of the student, but will include training in MRE supplied by attendance at ISMRM conferences and MRE workshops, and training in mathematical modelling and scientific computing within the School of Mathematical Sciences.
Deadline for applications is 25 February 2019, with interviews for applicants to take place between 4 and 8 March 2019
Applicants for the Precision Imaging PhD programme should have at least a 2:1 degree, or equivalent, in a project-relevant discipline. Funding is only available for UK and EU students.
1.Muthupillai R, Lomas DJ, Rossman PJ, Greenleaf JF, Manduca A, Ehman RL. Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science (New York, NY) 1995;269(5232):1854-1857.
2.Venkatesh SK, Yin M, Ehman RL. Magnetic resonance elastography of liver: technique, analysis, and clinical applications. Journal of magnetic resonance imaging : JMRI 2013;37(3):544-555.
3.Ronot M, Lambert S, Elkrief L, Doblas S, Rautou PE, Castera L, Vilgrain V, Sinkus R, Van Beers BE, Garteiser P. Assessment of portal hypertension and high-risk oesophageal varices with liver and spleen three-dimensional multifrequency MR elastography in liver cirrhosis. European radiology 2014;24(6):1394-1402.
4.Tomita S, Suzuki H, Kajiwara I, Nakamura G, Jiang Y, Suga M, Obata T, Tadano S. Numerical simulations of magnetic resonance elastography using finite element analysis with a linear heterogeneous viscoelastic model. Journal of Visualization 2018;21(1):133-145.