René Michael Botnar1
1Biomedical Engineering, King's College London, London, United Kingdom
Synopsis
Despite the current success of myocardial tissue
characterization with parametric mapping approaches, the accuracy and precision
of T1, T2, T2* and ECV mapping can be affected by many confounding factors
such as B0 and B1 inhomogeneity, respiratory and cardiac motion, heart rate
variability, magnetisation transfer and other parameters. Moreover,
estimation of T1, T2 and T2* is usually done by pixel-wise fitting of the MR
signal to simple exponential models that may be an oversimplification of the true
MR signal evolution. Here we will review the basic myocardial
mapping techniques, discuss their pros and cons and potential solutions.
Abstract
Clinical research studies have demonstrated the clinical usefulness
of quantitative myocardial tissue characterization (T1 and T2 relaxation time
mapping) and its ability to differentiate between healthy and diseased tissues. Quantitative
T1 mapping before and after contrast agent injection allows for detection of
diffuse myocardial fibrosis due to changes in water content and extracellular
volume (ECV) while T2 mapping is more sensitive for oedema detection (increased water
content), both important biomarkers of adverse myocardial remodelling and
inflammation1,2. Native T1 mapping has been shown to enable
identification of patients with amyloidosis, Fabry’s disease or iron overload
while T2 mapping is especially useful in patients with inflammatory heart
disease3. The usefulness of T2* mapping has been demonstrated in
patients with thalassemia4 while the assessment of the ECV, which can be obtained from a pre and post contrast T1 map, is
particularly useful for the detection of diffuse fibrosis, protein deposition and collagen
remodeling1. Despite the current success of myocardial tissue
characterization with parametric mapping approaches, the accuracy and precision
of T1, T2, T2*, and ECV mapping can be affected by many confounding factors
such as B0 and B1 inhomogeneities, respiratory and cardiac motion, heart rate
variability, magnetisation transfer and other parameters5. Moreover,
estimation of T1, T2 and T2* is usually done by pixel-wise fitting of the MR
signal to a simple exponential model that may be an oversimplification of the true
MR signal evolution. For these reasons, a large variety of myocardial mapping techniques exist that try to address one or several of the above problems. In order to
address the challenge of oversimplified exponential signal models and to
obtain quantitative MR parameters simultaneously, MR fingerprinting (MRF) has
been proposed. MRF performs a pseudo random image acquisition of hundreds to
thousands of highly undersampled low quality images (instead of 3-10 high
quality weighted images as usually performed in conventional T1 and T2 mapping)
and subsequently matches the measured MR signal (also called fingerprint) to a
dictionary of unique signal evolutions for specific T1 and T2 pairs generated
with the Bloch equations or extended phase graphs6. In this
presentation we will review the basic myocardial mapping techniques, discuss
their pros and cons and subsequently discuss potential solutions such as
MRF, multitasking and other approaches. Acknowledgements
This work was supported by the following grants:
(1) EPSRC EP/P032311/1, EP/P001009/1 and EP/P007619/1, (2) BHF programme grant
RG/20/1/34802, (3) King’s BHF Centre for Research Excellence RE/18/2/34213 (4)
Wellcome EPSRC Centre for Medical Engineering (NS/A000049/1), and (5) the
Department of Health via the National Institute for Health Research (NIHR)
Cardiovascular Health Technology Cooperative (HTC) and comprehensive Biomedical
Research Centre awarded to Guy’s & St Thomas’ NHS Foundation Trust in
partnership with King’s College London and King’s College Hospital NHS
Foundation Trust.References
1)
Haaf P et al. JCMR (2016) 18:89; 1-12
2)
Ferreira V et al. J Am Coll Cardiol (2018) 72:24;
3158-3176
3)
Liu et al. JCMR (2017) 19:74; 1-10
4)
Kirk P et al. Circulation (2009) 120;1961-1968
5)
Kellman P et al. JCMR (2014) 16:2; 1-20
6)
Ma D et al. Nature (2013) 187-92