Christian Guenthner1, Peter Koken2, Peter Boernert2,3, and Sebastian Kozerke1
1University and ETH Zurich, Zurich, Switzerland, 2Philips Research, Hamburg, Germany, 3Leiden University Medical Center, Leiden, Netherlands
Synopsis
We have investigated the feasibility of concurrent
water/fat separation and T1/T2 mapping using spoiled FISP-MR Fingerprinting on
a lower-field 0.75T MRI. Water/fat separation is performed in k-space and
combined with seven-peak fat-spectrum deblurring and B0-deblurring using
multi-frequency interpolation. Matching is performed for water and fat
separately and takes B1+ inhomogeneities into account. At 0.75T, T1 was 491ms (liver), 911ms (spleen),
958ms (kidney), 744ms (muscle), and 195ms (fat); and T2 was 77ms (liver), 91ms
(spleen), 111ms (kidney), 50ms (muscle), and 105ms (fat).
Introduction
In recent years, lower-field MRI (0.1…1T)
has received considerable interest as a potential cost-effective, yet
high-quality and -performance imaging modality.1 Quantitative parameter mapping using MR fingerprinting might
especially profit from lower-field as confounders including B0 and B1+
inhomogeneity are reduced while T2* is prolonged.2 Feasibility of spoiled FISP-MRF for brain and abdominal
applications was recently demonstrated on a lower-field MRI (0.55T) system.2 In the present work, we investigate the feasibility of combining FISP-MRF
with chemical shift-based water/fat separation for concurrent water/fat imaging
and T1/T2 estimation in the abdomen using a lower-field 0.75T MRI.Methods
We exploited the Carbon-13 channels of the
multi-nuclei interface of a 3T Philips Achieva system (Best, The Netherlands)
to measure proton magnetization at a quarter of the nominal field strength i.e.
at 0.75T. A double Helmholtz volume transmitter and a four-channel receive
array (Clinical MR Solutions, Brookfield, WI, USA) was used for excitation and
signal reception.
We performed FISP-MRF3 combined with a two-point Dixon-like acquisition as published by
Koolstra et al. in the abdomen of one healthy volunteer.4 A 500-point constant-TR spiral MRF sequence was modified to acquire
the same flip angle twice in an “interleaved fashion” alternating between in-
(4.605 ms) and out-phase (9.21 ms) echo times (water/fat shift at
0.75T: approx. 108 Hz) resulting in an effective fingerprinting train of 1000
shots and utilizing a fixed repetition time of 25 ms (Figure 1). An
Archimedean spiral with seven-fold undersampling, 12 ms readout duration,
field-of-view of 350 mm, and 2 mm in-plane resolution was employed.
Slice thickness was chosen as 8 mm as a trade of between SNR and
resolution (Figure 1), resulting in a single train water-fat resolved MRF of
25s duration.
Reconstruction of the MRF data was
performed in MATLAB 2018a (Mathworks, Natick, MA, USA) using MRecon (GyroTools
LLC, Winterthur, Switzerland) as shown in Figure 2. Water/fat separation was
performed in k-space and the fat-channel was deblurred using a seven-peak fat-spectrum.5,6 B0 blurring was corrected for using multi-frequency interpolation
and a separately acquired B0 map (Figure 3).7
The dictionary was created using the
extended phase graph formalism8 and obtained for a range of T1 values of [2:2:100, 100:10:1000, 1000:20:2000,
2000:40:5000] ms and T2 values of [2:2:150, 150:10:500, 500:20:1000,
1000:40:2000] ms. Transmit field inhomogeneities were corrected for by measuring
a separate B1+ map using DREAM9 (Figure 3) and accounting for it in the dictionary simulation using
B1+ values of 50% to 80% in 5% point steps. Separate dictionaries for water and
fat were obtained by summing and subtracting subsequent signals, resulting in
two, 500-point time series. Matching was then performed for water and fat
separately to obtain T1 and T2 maps.4 To reduce computational complexity, the dictionaries were
calculated with a B1+ resolution of 5% and linearly interpolated to intermediate
B1+ values.
For comparison, we also performed a classical
Cartesian multi-acquisition three-point Dixon scan to show efficacy of
water/fat separation with equivalent field-of-view and nominal resolution
(TR=11.5ms, TE=2.9ms,
TE=3.07 ms, 2mm
resolution, 8mm slice-thickness, 4 averages).10,11
Results & Discussion
The
water/fat separating FISP-MRF sequence resulted in a breath hold duration of 25 s
for a single coronal slice, which is comparable to the 27 s breath hold
required for the Cartesian multi-acquisition Dixon scan.
In Figure 4,
reconstruction results of the MRF acquisition are shown providing a water/fat
separated proton image and separate estimates of T1 and T2 for water and fat
channels. The proton density images obtained with MRF are in good agreement
with the Cartesian Dixon scan, however showing bright vessels due to blood
inflow effects during the MRF sequence and slightly reduced actual resolution
of the spiral acquisition compared to the Cartesian scan.
Average and
standard deviation of relaxation parameters are reported in Figure 5 for liver
(T1/T2: 491ms / 77ms), spleen (911ms / 91ms), kidney (958ms / 111ms), muscle
(744ms / 50ms) and fat (195ms / 105ms).
Due to the
reduced Larmor frequency at 0.75T, the body coil of the 3T system could not be
used, which necessitated the additional 13C hardware to excite
proton magnetization. The flexible four-loop transmitter resulted in
considerable B1+ inhomogeneity (Figure 3) especially for coronal and sagittal slice
orientations, which needed to be considered during matching. B1+ homogeneity was
considerably improved for transverse slices and it is expected to be improved
generally, if a body coil is employed.Conclusion
We have demonstrated feasibility of
water/fat separation with simultaneous T1/T2 parameter mapping in the abdomen
on a lower-field 0.75T MRI resulting in water/fat separation comparable to
Cartesian three-point Dixon. Despite the lower signal-to-noise ratio at lower
field, MR fingerprinting is capable of determining T1/T2 concurrently to
chemical shift-based water/fat separation. Improved performance is expected, if
the fingerprint sequence and readout are specifically optimized for 0.75T.Acknowledgements
No acknowledgement found.References
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