Multiparametric diagnosis of fatty liver disease with magnetic resonance fingerprinting

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

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.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 01/10/2019 31/03/2028
2435042 Studentship EP/S022104/1 01/10/2020 30/09/2024 Donovan Tripp