Details of Grant 

EPSRC Reference:	EP/V044087/1
Title:	Contrast-free Deep Myocardial Tissue Characterization with Cardiac MR Fingerprinting
Principal Investigator:	Prieto, Professor C
Other Investigators:	Botnar, Professor RM Chiribiri, Dr A King, Dr AP
Researcher Co-Investigators:
Project Partners:
Department:	Imaging & Biomedical Engineering
Organisation:	Kings College London
Scheme:	Standard Research
Starts:	01 October 2021	Ends:	30 September 2024	Value (£):	932,528
EPSRC Research Topic Classifications:	Medical Imaging
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 09 Feb 2021 Healthcare Technologies Investigator Led Panel Feb 2021 Announced
Summary on Grant Application Form
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality in the Western world, causing over 65.000 deaths every year in England. Magnetic Resonance Imaging (MRI) is an important non-invasive tool for risk assessment, guidance of therapy and treatment monitoring of CVD. Quantitative mapping of magnetic relaxation properties (such as T1 and T2 relaxation times) have been developed with the aim of standardizing the quantitative measurement of myocardial tissue properties, enabling non-invasive characterization and differentiation of diseased and healthy tissue. Several clinical studies have shown the potential of tissue specific parameters such as T1, T1rho, T2 and T2* relaxation times as well as extracellular volume (ECV) and fat fraction (FF) to improve the assessment of CVD. However, quantitative cardiac MRI still suffers from several challenges. A major limitation is that despite promising quantitative tissue characterization these maps are usually site- and vendor-specific due to several model simplifications and MR system-related confounding factors. These maps are acquired sequentially with different MRI sequences (before/after contrast injection) and potentially at different motion states (due to physiological motion). Furthermore, mapping a single parameter at a time can lead to inaccurate quantification due to errors introduced by inter-parameter dependencies. All of the above results in long scan times (limiting the number of slices and parameters estimated) and negatively affects reproducibility, analysis and interpretation of the parametric maps. Cardiac Magnetic Resonance Fingerprinting (MRF) has recently emerged as an approach to rapidly and simultaneously quantify multiple tissue properties (e.g. T1 and T2). However, several developments are yet needed to enable robust and reproducible contrast-free myocardial tissue characterization of multiple parameters with cardiac MRF. Limitations of current cardiac MRF approaches include: 1) quantification of only T1 and T2 (and more recently FF), however a wealth of additional myocardial tissue information (e.g. T1rho, T2*) could enable further understanding of the underlying CVD, 2) image reconstruction methods required to accelerate cardiac MRF result in long computational times, currently impeding clinical translation. 3) The computational burden of dictionary generation and matching required in MRF increases exponentially with the number of quantitative parameters, thus only few simultaneous parameters are currently quantified with cardiac MRF. 4) Biases in T1 and T2 with respect to conventional mapping techniques have been observed in-vivo, which may be explained by several confounding factors, which are currently not included in the cardiac MRF model, and 5) repeatability and reproducibility studies are limited, which is a fundamental step to provide a standardised framework for quantitative cardiac MRI. The proposed project will overcome these problems by developing a novel, robust and comprehensive multiparametric quantitative cardiac MRF approach to enable reproducible simultaneous T1, T2, T1rho, T2* and FF mapping from a single and efficient scan. Furthermore, we will investigate whether the proposed approach offers the possibility of deriving comprehensive myocardial tissue characterization without the need of additional post-contrast imaging. Deep-learning (DL) based motion correction, reconstruction, dictionary generation and matching will be investigated to enable the acquisition of multiple accurate maps in ~15-18s/slice as well as the computational scalability needed to account for several parameters and confounding factors in the MRF framework. The proposed approach will be validated in standardised phantoms, healthy subjects and patients with CVD in two different clinical research Institutions.
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