Contrast-free Deep Myocardial Tissue Characterization with Cardiac MR Fingerprinting

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

Abstract

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.

Publications

10 25 50
 
Description Cardiac Magnetic Resonance Fingerprinting (MRF) has recently emerged as an approach to rapidly and simultaneously quantify multiple tissue properties including T1, T2 and proton density. However, several developments are yet needed to enable robust and reproducible contrast-free deep myocardial tissue characterization with cardiac MRF. We have developed developed a novel, robust and comprehensive multiparametric quantitative cardiac MRF approach to enable reproducible simultaneous T1, T2, T2* and fat fraction mapping from a single and efficient scan. We have also developed and implemented novel deep-learningbased undersampled reconstruction methods to enable accurate and computationally efficient reconstruction of these approaches.
Exploitation Route The proposed approach could be further evaluated in patients to investigate its potential clinical value
Sectors Healthcare

 
Description We have developed a novel T1, T2, T2* and fat fraction magnetic resonance fingerprinting approach to provide comprehensive myocardial tissue characterization. Currently we are investigating the clinical value of the proposed technique in a small cohort of patients with cardiovascular disease. In addition, we have evaluated this approach for liver tissue characterization together with collaborators.
First Year Of Impact 2023
Sector Healthcare
Impact Types Societal

 
Title MRF reconstruction framework 
Description Reconstruction framework for Magnetic Resonance Fingerprinting reconstructions using ADMM-based and low-rank-based solvers. 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? Yes  
Impact This framework has enabled several collaborations to evaluate MRF for different cardiac and liver applications 
 
Description Cleveland Clinic Cardiac MRF 
Organisation Cleveland Clinic
Country United States 
Sector Hospitals 
PI Contribution We have developed an acquisition and reconstruction framework for cardiac Magnetic Resonance Fingerprinting that enable simultaneous quantification of T1, T2 and fat fraction mapping. This approach has been preliminary evaluated in 25 patients at KCL.
Collaborator Contribution In collaboration with Cleveland Clinic we aim to evaluate our. technical developments in a larger cohort of patients with different cardiovascular diseases.
Impact An abstract has been presented at the SCMR conference 2021. A review article in cardiac MRF has been published as result of this collaboration.
Start Year 2020
 
Description Cleveland Clinic Cardiac MRF 
Organisation Cleveland Clinic
Country United States 
Sector Hospitals 
PI Contribution We have developed an acquisition and reconstruction framework for cardiac Magnetic Resonance Fingerprinting that enable simultaneous quantification of T1, T2 and fat fraction mapping. This approach has been preliminary evaluated in 25 patients at KCL.
Collaborator Contribution In collaboration with Cleveland Clinic we aim to evaluate our. technical developments in a larger cohort of patients with different cardiovascular diseases.
Impact An abstract has been presented at the SCMR conference 2021. A review article in cardiac MRF has been published as result of this collaboration.
Start Year 2020
 
Description Juntendo University Liver MRF 
Organisation Juntendo University Hospital
Country Japan 
Sector Hospitals 
PI Contribution We have developed a novel acquisition and reconstruction framework for simultaneous T1, T2, T2* and fat fraction quantification in liver imaging. This approach has been preliminary evaluated in healthy subjects.
Collaborator Contribution In collaboration with Juntendo University Hospital we aim to clinically validate the proposed liver MRF in a medium size cohort of patient with fatty liver disease and validate this against histopathology.
Impact An abstract has been accepted for publication at upcoming ISMRM international conference
Start Year 2020
 
Description UC Liver MRF 
Organisation Pontifical Catholic University of Chile
Country Chile 
Sector Academic/University 
PI Contribution We have developed a novel magnetic resonance fingerprinting for multiparametric liver tissue characterisation. We have developed a reconstruction method for this data.
Collaborator Contribution Our collaborators at UC have further developed the reconstruction method to improved quantification of T2* mapping. A postdoctoral researcher from UC spend 3 months in our lab at the start of this collaboration.
Impact Two abstracts have been accepted for presentation at the next international conference ISMRM 2020. A paper has been submitted to Magnetic Resonance in Medicine (under review) entitled "Multi-parametric liver tissue characterization using MR Fingerprinting: simultaneous T1, T2, T2* and fat fraction mapping"
Start Year 2019
 
Description Art x Science at the Science Museum 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Public/other audiences
Results and Impact Alina Schneider (PhD student) participated of the Art x Science at the Science Museum, London during the Great Exhibition Road Festival on Saturday 9th October 2022. Art x Science was an art exhibition exploring medical imaging and engineering research, with exhibits were developed in collaboration between PhD students from the School of Biomedical Engineering and Imaging Sciences and the Royal College of Arts. The exhibits covered some perinatal imaging, neurodevelopment, cardiology and cardiac imaging. As well as explaining the science behind the exhibit, Alina was also able to discuss her research with visitors.
Year(s) Of Engagement Activity 2021
URL https://www.greatexhibitionroadfestival.co.uk/event/art-x-science-2021/