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.
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
Velasco C
(2021)
Simultaneous T 1 , T 2 , and T 1? cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization
in Magnetic Resonance in Medicine
Coronado R
(2021)
A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting.
in IEEE transactions on medical imaging
Eck BL
(2021)
Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications.
in Progress in nuclear magnetic resonance spectroscopy
Qi H
(2021)
Synergistic multi-contrast cardiac magnetic resonance image reconstruction.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Jaubert O
(2021)
T1, T2, and Fat Fraction Cardiac MR Fingerprinting: Preliminary Clinical Evaluation.
in Journal of magnetic resonance imaging : JMRI
Fotaki A
(2022)
Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches.
in Frontiers in cardiovascular medicine
Cruz G
(2022)
Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting.
in Magnetic resonance in medicine
Velasco C
(2022)
Artificial intelligence in cardiac magnetic resonance fingerprinting.
in Frontiers in cardiovascular medicine
Qi H
(2022)
Accelerated 3D free-breathing high-resolution myocardial T1? mapping at 3 Tesla.
in Magnetic resonance in medicine
Velasco C
(2022)
Simultaneous comprehensive liver T1 , T2 , T2* , T1? , and fat fraction characterization with MR fingerprinting.
in Magnetic resonance in medicine
Lima Da Cruz GJ
(2022)
Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting.
in Magnetic resonance in medicine
Soyak R
(2022)
Channel Attention Networks for Robust MR Fingerprint Matching.
in IEEE transactions on bio-medical engineering
West DJ
(2022)
An MR fingerprinting approach for quantitative inhomogeneous magnetization transfer imaging.
in Magnetic resonance in medicine
Castillo-Passi C
(2023)
KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration.
in Magnetic resonance in medicine
Castillo-Passi C
(2023)
KomaMRI.jl: An Open-Source Framework for General MRI Simulations with GPU Acceleration
Cruz G
(2023)
Single-heartbeat cardiac cine imaging via jointly regularized nonrigid motion-corrected reconstruction
in NMR in Biomedicine
Munoz C
(2023)
Simultaneous Highly Efficient Contrast-Free Lumen and Vessel Wall MR Imaging for Anatomical Assessment of Aortic Disease.
in Journal of magnetic resonance imaging : JMRI
Lo Presti S
(2023)
Fingerprinting MINOCA: Unraveling Clues With Quantitative CMR.
in JACC. Case reports
Fujita S
(2023)
MR Fingerprinting for Contrast Agent-free and Quantitative Characterization of Focal Liver Lesions
in Radiology: Imaging Cancer
Fujita S
(2023)
MR Fingerprinting for Liver Tissue Characterization: A Histopathologic Correlation Study.
in Radiology
Coletti C
(2023)
Robust cardiac T1?$$ {\mathrm{T}}_{1_{\boldsymbol{\rho}}} $$ mapping at 3T using adiabatic spin-lock preparations
in Magnetic Resonance in Medicine
Eck BL
(2023)
Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications.
in Current cardiology reports
Cruz G
(2023)
Low-rank motion correction for accelerated free-breathing first-pass myocardial perfusion imaging.
in Magnetic resonance in medicine
Phair A
(2023)
Free-running 3D whole-heart T1 and T2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction.
in Magnetic resonance in medicine
Munoz C
(2023)
Latest Advances in Image Acceleration: All Dimensions are Fair Game.
in Journal of magnetic resonance imaging : JMRI
Schneider A
(2024)
Non-rigid motion-compensated 3D whole-heart T 2 mapping in a hybrid 3T PET-MR system
in Magnetic Resonance in Medicine
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 | 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 |