Multiparametric diagnosis of fatty liver disease with magnetic resonance fingerprinting

In this proposal we aim to develop, implement and validate a novel fully co-registered multiparametric quantitative mapping approach from a single and efficient MR fingerprinting scan, to enable comprehensive diagnosis of non-alcoholic fatty liver disease, the most common chronic liver disease world-wide. The specific aims of the project are to:

Develop 2D liver Magnetic Resonance Fingerprinting (MRF) approach for simultaneous T1, T2, T2* and fat-fraction mapping to enable liver imaging in small animal models and humans.
Extend the MRF technique to 3D for whole liver coverage and higher spatial resolution, incorporating respiratory motion correction based on self-navigation.
Validate the sensitivity and accuracy of the novel 2D and 3D MRF techniques in a mouse model of fatty liver disease and in response to treatment.
If time permits, validate the proposed 2D liver MRF approach in a small cohort of patients at different stages of fatty liver disease.


Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the world with a prevalence of 30%-40% in the general adult population (~80M people in US). It is found predominantly in obese people with high-fat diets and inactive lifestyles. Effective risk stratification of NAFLD requires evaluation of hepatic fat content, inflammation and fibrosis, with liver biopsy remaining the current reference standard although more advanced non-invasive imaging techniques are rapidly emerging including quantitative ultrasound, MR elastography and most recently multiparametric MRI involving T1 mapping, fat fraction and iron (T2* map) quantification.

While the development of quantitative MR mapping techniques including conventional T1, T2* and fat fraction mapping for fibrosis, hemosiderosis and liver fat quantification have shown promising results, they are acquired sequentially with different spatial resolution and potentially at different respiratory positions due to respiration or bulk motion in-between those scans.

Here we propose to develop and validate in pre-clinical and clinical settings a novel liver Magnetic Resonance Fingerprinting (MRF) approach which may enable multiparametric mapping (T1, T2, T2* and fat fraction) of the different stages of NAFLD in a single scan (compared to multiple sequential scans). More specifically, we propose 1) to extend a 2D Dixon MRF (T1, T2 and fat fraction) technique that we have previously developed for cardiac imaging to provide simultaneous water-fat T1, T2, T2* and fat fraction (FF) maps which may facilitate image analysis and diagnosis due to intrinsic co-registration of the different maps. Furthermore, 2) to allow for whole liver coverage and improve spatial resolution we will extend the 2D liver MRF framework to a free-breathing motion corrected 3D liver MRF protocol. The use of an animal model of fatty liver disease will allow to 3) investigate the utility of 2D and 3D liver MRF for accurate staging of fatty liver disease and monitoring of treatment response. Compared to conventional parametric liver mapping the proposed approach will also provide co-registered T2 maps to facilitate differentiation between fibrosis and oedema, which the currently used clinical approach lacks. Finally, 4) the novel liver 2D MRF technique will be validated in a small cohort of patients at the different stages of fatty liver disease.

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.