Development of a semi-automated CT-brain analysis tool for application to real world clinical cohorts

Commercial partner: Brainomix, Oxford

Background

CT-brain imaging is the standard brain imaging modality used in the NHS and globally and is cheaper and better tolerated than MRI particularly in older, frail, multimorbid patients in whom MRI may be contraindicated. We have shown that small vessel disease (SVD) and atrophy on routinely acquired brain imaging can predict delirium and dementia occurring up to 5-years later (Pendlebury et al, Age Ageing,2021; Pendlebury & Rothwell, Lancet Neurology,2019). Routinely acquired CT-brain scans therefore have clinical utility beyond the immediate indication. However, CT-brain data are not fully exploited in research or in clinical risk prediction because the lack of automated CT-tools to quantify SVD and atrophy necessitates time-consuming visual ratings subject to inter-rater variability. In the current project, the student would develop a reliable, user friendly and validated CT-brain analysis tool for quantifying SVD and atrophy building on our team’s established and widely used MR brain imaging tools. Such a tool would be transformational in unlocking the data on brain ageing and pathology contained in CT-brain scans with wide application to research and clinical practice.

Aim

To develop and validate a user-friendly research ready CT-brain analysis tool for quantification of WMC and atrophy using real-world clinical data.

Objectives

• quantify global and regional brain atrophy relative to intracranial volume using an adaptation of our MRI tissue-type segmentation method;
• quantify SVD using adaptations of our MRI-based methods (BIANCA-Griffanti et al 2016);
• evaluation of tool performance using i) visual ratings ii) MRI data from paired CT/MRI scans, iii) manual segmentations iv) correlations with clinical variables.

To achieve the project objectives, the student will work on CT-brain scans from our previously assembled, large, well characterised cohorts of older patients with unplanned hospital admission including for stroke: OCS-Tablet/Recovery (n>850); Oxford Cognitive Co-morbidity, Frailty and Ageing Research Database (ORCHARD n>1000), Oxford Vascular Study (n>1,000). To create the CT-brain analysis tool, a range of machine learning methods (deep learning and classical methods) will be used as already developed for MRI brain (Sundaresan et al, OHBM 2018, 2019, MICCAI 2020) as well as promising new architectures (e.g. transformers). Use of data from different cohorts/scanners will ensure the CT-tool will be generalizable to a variety of settings.

How to apply

Before applying for these positions we recommend you contact the lead supervisors for informal discussions.

To make a formal application, please complete the University’s online application form for the DPhil in Clinical Neurosciences (please follow this link to the course website for further information on entry requirements and the application process itself). 

Please indicate the iCASE project clearly by inserting ‘iCASE’ before the project title and by using the reference code iCASE. You will need to provide a personal statement (500 words max if applying for a project hosted by one of Medical Sciences departments - please note that this limit might be different if a project is hosted by one of MPLS departments in which case follow their requirement) detailing your interest and fit for the studentship. Note that no project proposal is required for the iCASE studentship applications.

If you wish to apply for a combination of iCASE and other projects within the hosting department, this can be done on the same application form (max number of projects you can apply for on one application depends on the department you wish to apply to).

If you wish to apply for iCASE projects within different departments, you will have to make separate applications directly through those departments.

If you have any specific queries about the iCASE application process, please email [Email Address Removed].

All applications must be received by the deadline of 12 noon (UK time) Friday 1 December 2023.

We expect to interview shortlisted applicants in January/February and to make funding offers by the end of February.