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(MRC DTP) Automating analysis of the aorta to improve clinical risk stratification

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  • Full or part time
    Dr C Miller
    Prof T Cootes
    Dr J Naish
    Dr M Schmitt
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Background
Diseases of the thoracic aorta are common. For example, bicuspid aortic valve disease, which is associated with thoracic aortic aneurysm, affects 1% of the population. Complications of thoracic aortic disease include aortic dissection (tear) and rupture, which have very high mortality rates. As a result, tens of thousands of diagnostic and surveillance thoracic aortic scans are performed annually in the UK.

Unmet needs
1. Aortic measurements made on the scans are time consuming (>20 minutes per scan) and observer dependent, resulting in high costs and inefficiencies.
2. Risk stratification remains poor. Treatment (i.e. surgery) is currently guided by 1-dimensional aortic measurements, which are widely recognised to be inadequate. Parameters that provide more discriminatory and personalised risk stratification are required.

Proposed project
We have an on-going prospective cohort study of consecutive consenting patients undergoing cardiovascular MRI scanning in Manchester, which includes 6,800 patients as of May 2018 (recruitment 2000/year since Jan 2015). Clinical data, and ‘expert-analysed’ scan measurements are recorded, together with follow-up data (outcome data from NHS digital, hospital and GP records, follow-up scans). The cohort includes a range of aortic pathologies including Marfan, Turner and bicuspid aortic valve disease.

1) Methodology development
The student will develop a system to segment the aortic root and thoracic aorta from MRI images. The system will be trained using data from approximately 1000 patients (including normal and abnormal aortas). The system will build on state-of-the-art shape modelling and matching techniques developed in Manchester [1] augmented with Convolutional Neural Networks where they can be shown to be more robust. By locating clinical landmarks and the outline of structures of interest we will be able to automate the standard 1D clinical measurements and to produce novel new parameters describing the shape and appearance in more detail.

2) Validation
The analysis models developed in Part 1 will be validated on data from a second group of 1000 patients, including patients with normal and abnormal aortas. Accuracy of automatic measurements will be compared with those produced by human experts.

3) Prognostic utility and modelling
The utility of scan analysis parameters as prognostic factors (for a combined outcome of increase in aortic size, aortic intervention and death) will be evaluated using survival analysis methodology. Multivariable prognostic models, incorporating clinical variables (such as diagnosis [e.g. syndrome connective tissue disorders and non-syndrome disorders] and other risk factors [e.g. hypertension]) and scan analysis parameters will be developed.

https://www.research.manchester.ac.uk/portal/christopher.miller.html
http://personalpages.manchester.ac.uk/staff/timothy.f.cootes/
https://www.research.manchester.ac.uk/portal/josephine.naish.html
https://mft.nhs.uk/people/dr-matthias-schmitt/

Funding Notes

This project is funded under the MRC Doctoral Training Partnership. If you are interested, please contact the Principal Supervisor to arrange to discuss the project further asap. You MUST also submit an online application form - full details on how to apply can be found on the MRC DTP website www.manchester.ac.uk/mrcdtpstudentships. Interviews will be held w/c 2 July.

Applications are invited from UK/EU nationals only who have been resident in the UK for the last 3 years. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

References

[1] C.Lindner, P.A.Bromiley, M.C.Ionita and T.F. Cootes,"Robust and Accurate Shape Model Matching using Random Forest Regression-Voting", IEEE Trans. PAMI, Vol.37, No.9, pp.1862-1874, 2015



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