Kevin Keene1,2, Jan-Willem Beenakker1,3, Melissa Hooijmans4, Karin Naarding2,5, Erik Niks2, Louise Otto6, Ludo van der Pol6, Martijn Tannemaat2, Hermien Kan1,5, and Martijn Froeling7
1Department of Radiology, C.J. Gorter center for high field MRI, Leiden University Medical Center, Leiden, Netherlands, 2Department of Neurology, Leiden University Medical Center, Leiden, Netherlands, 3Department of Ophthalmology, Leiden University Medical Center, Leiden, Netherlands, 4Amsterdam University Medical Center, Amsterdam, Netherlands, 5Duchenne Center Netherlands, Utrecht, Netherlands, 6Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands, 7Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
Synopsis
Multi-echo
spin-echo transverse relaxometry mapping using multi-component models is used
to study disease activity in neuromuscular disease. A recent model using
extended phase graphs (EPG) was introduced to obtain separate T2 values for
water and fat, accounting for B1 and stimulated echoes. We improved this model
and showed the importance of including flip angle slice profiles with a
chemical shift displacement in the slice direction and correct calibration
methods for the T2 of the fat component. We showed its performance in four
clinical cohorts, and showed a gradual decline in T2water with
increasing fat fractions.
Introduction
Quantitative MRI is showing increasing promise as a
biomarker in the follow-up of disease progression in neuromuscular diseases
(NMD).1 Transverse relaxometry maps from multi-echo spin-echo (MSE) can be
separated into different relaxation components for water (T2water)
and fat (T2fat), where T2water has been proposed as a
marker for disease activity.2 Originally, bi-exponential3 or tri-exponential4,5 methods were introduced to separate water and fat signal contributions
at successive echo times. Later, an extended phase graph (EPG)6 algorithm was introduced, which accounts for B1 and stimulated echoes.7 However, this model is not optimized for high fat fractions above 50%7 and the effect of inaccuracies in the T2fat calibration
remain unexplored. In the present work, we aimed to improve the performance of
EPG fitting methods over a large range of fat fractions, by including the slice
selection flip angle profile, a chemical shift displacement correction and optimized
calibration of T2fat.Methods
An EPG signal model including a flip angle slice profile with chemical
shift displacement was used to fit MSE signals with a dictionary method.7–9 Simulation experiments were used to study the
influence of the flip angle slice profile with chemical shift (simulation 1)
and the influence of the assumed T2fat (simulation 2). Next, in vivo
data from four patient cohorts (92 patients and 56 healthy controls (HC) in
total) were used to evaluate the performance of different T2fat calibration
methods. The patient cohorts comprised data from: arm scans from 18 Duchenne
muscular dystrophy (DMD) patients and 11 HC (cohort 1, TE/ΔTE/TR/echoes/resolution
8ms/8ms/17/3000ms/2x2x10mm3),
leg scans from 22 DMD patients and 12 HC (cohort 2, TE/ΔTE/TR/echoes/resolution
8ms/8ms/17/3000ms/1.6x1.6x10mm3), leg scans from 23 Becker muscular
dystrophy patients and 13 HC (cohort 3, TE/ΔTE/TR/echoes/resolution
8ms/8ms/17/3000ms/1.6x1.6x10mm3) and thigh scans from 29 spinal
muscular atrophy patients and 20 HC (cohort 4, TE/ΔTE/TR/echoes/resolution
7.6ms/7.6ms/17/4598ms/3x3x6mm3). All cohorts were scanned at 3T
using multi-slice acquisition, and data from one ROI encompassing all muscles was evaluated. The T2fat was calibrated for each subject on
subcutaneous fat using three different methods. Method A assumed
one pure fat component, method B
estimated T2fat using a two-component model with a fat fraction of
90%10, and method C, was the same as method B but additionally the T2 of the
water component was fixed to 20ms, to stabilize fitting with low water signal. Trend
lines in figures were drawn using LOWESS regression and T2water
values between different calibration methods were compared using paired t-tests.Results
Excluding
the effect of flip angle slice profiles resulted in an overall underestimation
of fat fractions and overestimation of T2water, with a median error
of 10ms (Simulation 1A, figure 1). Not taking the chemical shift in the
flip angle slice profile into account led to an overestimation of T2water
of 4ms for a large simulated shift (Simulation 1B, figure 2). Furthermore,
a wrongly calibrated T2fat strongly influenced the estimation of the
T2water (Simulation 2, figure
3).
For the in
vivo data, histograms of in vivo calibrated T2fat showed that a one-component
model leads to a relatively low T2fat compared to the two-component methods
(figure 4). When comparing this to fat fractions, the one-component calibration
resulted in a decrease in T2water for all cohorts with increasing
fat fractions (figure 5). With two-component calibration, the negative
correlation between T2water and the fat fraction was reduced in cohort
1-3, and absent in cohort 4. Average T2water for HCs in each cohort was
comparable between the one-component (27.4±1.0ms, 28.8±0.6ms, 28.7±1.1ms and
28.5±0.7ms, for cohort 1 until 4 respectively) and the two-component
calibration (27.5±1.0ms, 29.2±0.6ms, 29.2±1.0ms and 29.2±0.7ms, for cohort 1
until 4 respectively). For the patients, T2water in the one-component
calibration (19.6±4.7ms, 25.5±4.9ms, 26.6±3.4ms and 20.6±4.2ms, for cohort 1
until 4 respectively) was lower (p<0.001, paired t-test) than the
two-component calibration (24.0±2.2ms, 28.8±3.9ms, 27.7±3.0ms and 27.1±3.4ms, for
cohort 1 until 4 respectively).Discussion
Our simulations showed that not including flip angle profiles
introduces an overestimation of the T2water of 10ms and an up to 20%
underestimation of the fat fraction, which is in line with previous work in the
brain.8,9 Ignoring slice profiles can result in wrongly estimated T2water
values. Within the same study, this bias might not be an issue, however, when
comparing between studies with different acquisition protocols this can result
in wrong interpretation of the results. Since T2fat calibration can
have a large influence on the estimation of T2water, it is important
that the calibration is done accurately. Here we have shown that the calibrated
T2fat cannot be translated between studies and between subjects. Assuming one general T2fat without
specific calibration is therefore not recommended. We hypothesize that the T2fat
differs between scanners and acquisition protocols due to J-coupling of fat
protons that is sensitive to small deviations in B1 and fat composition.Conclusion
We
recommend using an EPG based model for fitting T2water from the MSE
signal with calibration of the T2fat assuming two components.
Moreover, we recommend including the slice flip angle profile in the model with
correction for chemical shift displacements. In vivo data showed a gradual decline in
T2water for increasing fat fractions, which has important
implications for clinical studies, especially in a multi-center setting, using
T2water as an outcome parameter.Acknowledgements
No acknowledgement found.References
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