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Emerging Imaging

Multiparametric diagnosis of fatty liver disease with magnetic resonance fingerprinting

Project ID: 2020_042

Student: Donovan Tripp

1st Supervisor: René Botnar, King’s College London
2nd Supervisor: Claudia Prieto, King’s College London
Clinical Champion: Vicky Goh, King’s College London

Aim of the PhD Project:

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:

  1. 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.
  2. Extend the MRF technique to 3D for whole liver coverage and higher spatial resolution, incorporating respiratory motion correction based on self-navigation.
  3. 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.
  4. If time permits, validate the proposed 2D liver MRF approach in a small cohort of patients at different stages of fatty liver disease.

Project Description / Background:

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. The pathogenesis of liver disease can be subdivided into four stages, which are characterized by fat accumulation (NAFLD; ~65M), inflammation (NASH; ~15M), irreversible fibrosis (cirrhosis; 1-3M), and hepatocellular carcinoma (~70-200k) or other life-threatening complications1.  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 results2, they are acquired sequentially with different spatial resolution and potentially at different respiratory positions due to respiration or bulk motion in-between those scans. Other limitations include that currently employed T1 mapping with the MOdified Look Locker Inversion recovery technique (MOLLI) is not only sensitive to fibrosis but also to oedema, fat and iron deposition, which may reduce the specificity of this measurement. Moreover, current T1 and T2* mapping techniques are usually 2D and thus both spatial resolution and coverage is limited, which may affect diagnostic accuracy in patients with patchy disease patterns. Validation of the above sequences has mainly been performed with liver biopsy which only samples small areas in the liver and thus may miss pathologies, especially in patients with focal areas of inflammation, fibrosis or hemosiderosis.  

Here we propose to develop and validate in pre-clinical and clinical settings a novel liver Magnetic Resonance Fingerprinting (MRF)3 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 imaging4 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.      

This project joins expertise from MR physics, image reconstruction, biology and medicine with pre-clinical and clinical translation, and thus will permit the student to work and train at the interface of the different sub-disciplines. Candidates with background in physics or engineering, with a clear interest in medical imaging and pre-clinical and clinical translation of technologies, would be suitable for this project.


1. Brunt E.M. et al, Nat Rev Dis Primers. 2015;1:1-22,
2. Banerjee R. et al, Journal of Hepatology. 2014;60:69-77,
3. Ma D. et al, Nature. 2013;495:187-192,
4. Jaubert O. et al, MRM 2019.

Figure 1: Schematic of 2D liver Magnetic Resonance Fingerprinting to provide T1, T2, T2* and fat fraction mapping in a single scan for comprehensive assessment of non-alcoholic fatty liver disease.

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