Olivier Jaubert1, Cristobal Arrieta2,3, Gastao Cruz1, Aurelien Bustin1, Torben Schneider4, Georgios Georgiopoulos1, Pier-Giorgio Masci1, Carlos Sing-Long3,5,6, Rene Michael Botnar1, and Claudia Prieto1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, 3Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 4Philips Healthcare, London, United Kingdom, 5Instituto de Ingeniería Matemática y Computacional, Pontificia Universidad Católica de Chile, Santiago, Chile, 6Millennium Nucleus Center for the Discovery of Structures in Complex Data, Chile, Santiago, Chile
Synopsis
Quantitative T1, T2, T2* and fat
fraction (FF) maps are promising imaging biomarkers for the assessment of liver
disease. Magnetic Resonance Fingerprinting has been recently proposed for fast
T1, T2 and M0 mapping of the liver, however in
the presence of high iron or fat concentrations corrections using separately
acquired T2* and FF maps are needed. Here we propose a novel approach
which enables simultaneous liver T1, T2, T2*
and FF maps from a single ~15s breath-hold scan. The proposed approach was
evaluated on phantoms, 8 healthy subjects and 2 patients in comparison to
conventional mapping techniques.
Introduction
Quantitative T1, T2, T2* and fat
fraction (FF) maps are promising imaging biomarkers for the assessment and
follow-up of liver disease, which includes characterization of tumors, iron overload,
lipid content and fibrosis1. Magnetic Resonance Fingerprinting (MRF2) has been recently proposed for fast T1,
T2 and M0 mapping of the liver3. However in the presence of high iron or fat
concentrations, the T1 measurements are inaccurate and corrections
using separately acquired T2* and FF maps have been proposed1,4. These maps are acquired sequentially with
different spatial resolution and potentially at different respiratory
positions. Here we proposed a novel approach which enables simultaneous liver T1,
T2, T2* and FF mapping in a single ~15s breath-hold scan.Methods
A gradient echo (GRE) liver MRF sequence with nine bipolar readouts per
TR, low flip angles (5-15o), varying magnetisation preparation and
golden angle radial trajectory5,6 is proposed (Fig.1A). The proposed liver MRF
acquisition includes nine inversion recovery (IR) and T2 preparation (T2prep)
pulses (IR12ms, noPrep, T2prep40ms, T2prep80ms, T2prep160ms, IR300ms, noPrep,
T2prep40ms, T2prep80ms, T2prep160ms, IR12ms, noPrep) with varying recovery time
(~200-500ms) between each acquisition of 700ms duration (Fig.1A). Reconstruction
is performed using dictionary-based SVD (singular value decomposition) temporal
compression and patch-based multi-contrast reconstruction (HD-PROST)7. Graph-cut water/fat separation8 is used to estimate T2* and B0
from the first singular image. These estimates are fixed and used to recover the water and fat singular images from
the 9 echoes singular images (Fig.1B)6. T1, T2 and M0
maps are obtained through dot product matching of the singular images for both
water and fat, whereas the FF map is obtained from the water and fat M0
maps. Measurements were performed on a 1.5T scanner (Ingenia, Philips
Healthcare) in a standardized T1/T2 phantom9, a water/fat partial volume in-house phantom, 8
healthy subjects (age: 31.5 ± 4 years, body mass index (BMI): 25.1 ± 3.7 kg/m2)
and 2 patients (age: 55 and 65 years, BMI : 27.8 and 33.2 kg/m2),
and compared to conventional spin echo (IRSE and MESE) and T2*/FF8 (12 echo GRE) measurements in phantom and T1
(MOLLI), T2 (T2GRASE) and T2*/FF (12 echoes GRE) maps
acquired in three separate breath-holds in-vivo. Liver MRF acquisition
parameters were: TR/TE1/ΔTE= 20/1.5/2ms, 2x2mm2 resolution, 8mm
slice thickness, FOV= 496x496mm2, acq. time = 14.4s. Region of
interest (ROI) analysis was performed for each phantom vial, liver (median of four
regions to avoid bias due to the potential presence of blood vessels), subcutaneous
fat (SF), muscle and the spleen.Results
Phantom results with the proposed
approach correlate highly (R2>0.97) with the reference
measurements for all parameters (Fig.2.A). Figure 2.B shows correlation plots
between conventional and proposed in-vivo measurements with lines of best fit
for the four parameters of interest. In-vivo map comparisons between
conventional and proposed liver MRF measurements for two healthy subjects are
shown in Figure 3 and 4 (with slightly elevated FF and a benign hemangioma
respectively). In healthy subjects, biases of 110ms, -8.4ms, 2.3ms and 0.05%
for T1, T2, T2* and FF respectively were measured
when compared to conventional measurements. The difference between measurement
methods was statistically significant (p<0.05) for T1, T2 and T2* but not
for FF. T1 overestimation compared to MOLLI and underestimation of T2
are consistent with previous MRF findings5,10. Remaining differences between the
proposed liver MRF and conventional in-vivo measurements could be explained due
to differences in respiratory position, field inhomogeneities, magnetisation
transfer and diffusion effects. Two patients scanned during clinical routine are
shown in Figure 5, one of which presented high BMI (33.2kg/m2) and elevated
FF in the liver (median of 15.25%).Conclusion
A novel 9-echo liver MRF sequence
allows for quantitative multi-parametric liver tissue characterization in a
single breath-hold scan. Future work will aim to validate the proposed approach
in patients with liver disease.Acknowledgements
This work was supported by EPSRC
(EP/L015226/1, EP/P001009/1, EP/P032311/1) and Wellcome EPSRC Centre for
Medical Engineering (NS/ A000049/1). CSL and CA received funding from Millennium
Science Initiative of the Ministry of Economy, Development and Tourism, Government
of Chile, grant Nucleus for Cardiovascular Magnetic Resonance and grant
Millennium Nucleus Center for Discovery of Structures in Complex Data. CSL was
partially funded by Fondecyt #11160728. CA was partially funded by Fondecyt
Postdoctorado 2019 #3190763. CSL and CA were funded by CONICYT PCI-REDES180090.References
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