Rapid MRI scanning for population-level neuroimaging

Commercial partner: Siemens Healthineers

The UK Biobank (UKB) is the largest research database of brain images in the world, having recently reached 90% of its target of 100,000 neuroimaging participants. In addition to brain scans, UKB provides a wealth of data about participants’ life history, environment, genetics, physical and cognitive attributes, and health records. Crucially, UKB participants are largely healthy when imaged and then followed long-term, providing the potential to identify early “biomarkers” that presage later ill health.

One example biomarker is the volume of the hippocampus, a brain structure that is crucial to memory that atrophies in dementia. UKB scans have been used to derive highly detailed population norms of hippocampal volume with respect to age and other demographics (Nobis 2019), against which clinical scans in new individuals can be compared to establish likelihood of early atrophy. While such biomarkers are sensitive and stable within a given scanner and protocol, they are not sufficiently quantitative to be directly comparable across scanners or protocols. In order to use these population norms in a new setting, MRI-derived biomarkers require “harmonisation” to accurately relate clinical scans to UKB-derived norms.

The purpose of this project is to develop a rapid 5-10-minute brain MRI scan that is harmonised to the UK Biobank. This rapid acquisition could be appended to the end of any clinical routine scan to assess one or more MRI-derived biomarkers. Ultimately, we would envisage the scanner producing a report for neurologists or radiologists that places the individual’s measured biomarker in the context of UKB-derived population norms. In addition to use in clinical decision making, such harmonised biomarkers would be useful for monitoring of therapeutics, drug discovery, and in clinical research studies.

We will achieve this scan time reduction through a combination of strategies including data undersampling, rapid acquisition trajectories, and a joint reconstruction that regularises the generated images, building on the large database of existing UKB images and image reconstruction expertise in Siemens and Oxford. This research will build on engineering concepts including signal compression, non-linear optimisation, and machine learning.

The project would identify a subset of imaging derived phenotypes (IDPs) most relevant to clinical populations. Using these data the minimum information required will be identified, including coarsest acceptable resolution and the image contrast, required to derive a usable IDP. Sequence undersampling and reduction strategies will be developed to achieve at least a 4-fold acceleration on top of that already used in UKB whilst maintaining image information content for IDP derivation. These undersampled acquisitions will be reconstructed using a combination of joint reconstruction (across contrasts) and deep learning, leveraging the large amount of existing image sets in the UKB (circa 100,000 subjects).

We will complement this data-driven harmonisation approach by testing alongside cutting-edge biophysical harmonisation methods under development at our centre.

Apply using course: DPhil in Clinical Neurosciences

MRC INDUSTRIAL CASE STUDENTSHIPS 2025

Designed to nurture the academic entrepreneurs of the future, the Enterprise studentship programme offers a stimulating educational experience as part of the Oxford-MRC DTP cohort, with the additional benefit of working closely with an industrial partner. This will provide entrepreneurial training opportunities and an insight into how commercial science is conducted alongside a superb academic base within the University. Students will work for at least 3 months in the associated company.

ELIGIBILITY

They are open to both UK and non-UK nationals and will follow the UKRI student eligibility requirements. UKRI will normally limit the proportion of international students appointed each year through individual training grants to 30% of the total intake each year.

FUNDING PACKAGE

Each iCASE studentship is fully-funded – it includes four years of stipend at the UKRI stipend level + £2,500 p.a., course fees, and a generous research training support grant.

APPLICATIONS DEADLINE

Applications must be received by 12 noon (UK time) Tuesday 3 December 2024. Details on entry requirements and how to apply can be found below.

For details of entry requirements please go to the Oxford-MRC DTP iCASE 2025 Projects page.

HOW TO APPLY

Before applying for this project we recommend you contact the lead supervisors for informal discussion.

To make a formal application, please complete the University’s online application form for the DPhil course specified under the project description above. 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 queries about the iCASE application process (questions about the project should be directed to the lead supervisor), please email [email protected].