High resolution optimal precision quantitative MRI at Ultrahigh Field

Aim of the PhD Project:

Harness UHF MRI for high resolution quantitative neuroimaging
Develop qMRI sequences using advanced RF technology (parallel transmit, pTx) with the objective of maximising the achieved precision per unit time across the whole brain
Quantify effects from macromolecules in brain tissue (MRI usually only looks at liquid water)
Project Description / Background:

The tissue signal in MRI is in general a complex function of many factors including water content, relaxation times (T1/T2), macromolecular composition, macro and microvasculature, fat content, diffusion properties and many more. Conventional MR imaging uses standard protocols whose tissue contrast is 'weighted' towards one or more parameter, and radiologists interpret these from experience. Quantitative MRI (qMRI) instead aims to directly measure many of these important parameters, to directly quantify tissue properties. This offers the possibility to make quantitative comparisons between subjects or longitudinally for the same subject, and when combined with the emergence of 'big data' methods could lead to improved understanding of the brain in health and disease.

A key limitation for MRI is the spatial resolution that can be achieved, which is typically in the range of millimetres. New ultrahigh field (UHF; 7T and above) scanners can potentially achieve higher resolutions (down to 100s of microns) and a new 7T MRI facility has recently been installed at St.Thomas' with the objective of supporting a wide base of clinical and research neuroscience from across London. There are however still particular challenges for working at 7T, including highly spatially non-uniform radio frequency magnetic fields (B1) and stringent hardware and safety constraints. B1 non-uniformity leads to strong variations in contrast that can be a problem for interpretation of standard 'weighted' MRI, and which will cause large variations in achievable precision for qMRI. Limits on specific absorption rate (SAR) mean that methods needed for measurement of T2 (such as balanced SSFP or spin echo) are a challenge. Additionally, advanced motion correction methods are necessary to truly reach sub-millimetre resolution since even a compliant volunteer will move involuntarily at this level during image acquisition.

'MR Fingerprinting' (MRF) is a significant recent development in qMRI; by using a constantly variable pulse sequence that does not allow magnetization to reach a steady state it has been shown to be a sensitive and somewhat motion tolerant approach. Recent work has focused on optimizing MRF to maximise estimation precision both by directly optimizing the pulse sequence and the image reconstruction. However it is now becoming widely acknowledged that 'magnetization transfer' (MT) between water and macromolecules in brain tissue is a strong confound for quantitative measurements4, and this includes both conventional qMRI and MRF5.
The high degree of B1 non-uniformity at UHF will make estimation precision highly variable across the brain, and since MT effects are related to B12 the effect will be stronger.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.