Hiroyuki Ueda1, Yosuke Ito1, Takenori Oida1, Yo Taniguchi2, and Tetsuo Kobayashi1
1Electrical Engineering, Kyoto University, Kyoto, Japan, 2Hitachi, Ltd., Kokubunji, Japan
Synopsis
To detect neural activities using a low-field MRI,
we focused on spin-lock techniques, which are potentially effective tools for low-field
functional MRI (fMRI). First, we estimated the magnetic field strength generated
from neural activities using an equivalent current dipole model. Subsequently,
we compared two spin-lock techniques by simulation and phantom experiments. It
was found that the lowest detectable field strength was several tens pT in the
simulation, whereas the experimental one was 2.34 nT. In future work, we plan
to improve SNR using noise-reduction processing to realize the low-field fMRI.
INTRODUCTION
BOLD-fMRI has a spatiotemporal limitation deriving
from slow and widespread hemodynamic responses to neural activities. To
overcome this limitation, some neuronal current MRI (ncMRI)1 techniques observed phase shift in MR signals caused by the neural
activities had been reported. However, these methods were difficult to measure high-frequency
signals or steady-state responses. Then, we focused on the spin-lock
preparation sequences2–5, which can select target frequencies without such constraints. In addition,
the signal displacement obtained by those sequences is independent of static magnetic
fields, that is, we can apply those even to the low-field MRI. Despite the
advantages that the low-field MRI scanners are cheaper and safer, there is no
report that shows the feasibility of the low-field fMRI using the spin-lock preparation
sequences. In this study, we investigated the magnetic field sensitivity required
to identify the neural activities based on simulations using an equivalent current
dipole model. We also carried out phantom experiments and compared their
results with simulation results to demonstrate the feasibility of the spin-lock
sequences toward low-field fMRI.METHODS
First, we estimated the magnetic fields
generated by the neural activities using the equivalent current dipole model as
the field strength in a voxel near the source. The dipole moment density was reported
as 0.5-1.0 nAm/6. We solved Heller’s equation7, assuming that the activation area was , the
current dipole moment was 6.25 nAm and its position was 4cm depth from the
surface of a conducting sphere with the diameter of 25 cm. Then, we varied the
dipole moment from 0 to 100nAm and got the maximum absolute value of magnetic
field.
Second, we compared two spin-lock preparation
sequences; the stimulus-induced rotary saturation (SIRS)2 and the method proposed by Trong et al. 5, called the spin-locked
Mz (SL-Mz) in this study. We simulated MR signals acquired by these sequences
based on the Bloch equation8. Then, we carried out phantom experiments with a 0.3-T MRI scanner
(HITACHI, AIRIS-Vento). A loop coil, which worked as a source of the magnetic
field, was installed in the phantom filled with saline solution. The loop coil
was connected to a function generator via a low pass filter whose cutoff
frequency was 5 kHz. We applied a 100-Hz sinusoidal wave from the function
generator. We observed MR signal changes as a function of signal amplitude and
phase, and carried out curve fitting considering the RF accuracy.RESULTS
Figures.1 and 2 show the z component of
magnetic field () generated
by a current dipole and the in a voxel as function of dipole moment, respectively.
Assuming that neural activity corresponds to the order of 10nAm9, the Bz in
a voxel near the current dipole was calculated to be from tens to thousands pT.
Its variation depended on the activation area, the current dipole moment and
dipole position. The experimental results are shown in figs.3 and 4. The MR
signal change ratios were calculated as MR signals with the target magnetic
fields divided by T2-weight images. On the curve fitting results, we showed the
estimated fitting parameters in Table. 1(a) and 1(b). The conversion rate in
the table was the magnetic field (nT) divided by the function generator output
(Vpp). In the case of SIRS, the estimated flip angle was 16.67
degree shifted from the set value due to the scanner limitation. However,
overall, the results showed good agreement with the theory. SIRS could not detect
7.01 nT with t-test, but on the SL-Mz whose noise distribution cannot be
assumed as Gaussian, there was significant difference between 0 nT and 2.34 nT
with the
Wilcoxon signed-rank test. Likewise, on MR signal
change as function of signal phase, the empirical result also agreed with the
theory, as shown in Table 1(c).DISCUSSION
The estimated magnetic fields generated by
neural activities in a voxel were larger than that detectable with the spin-lock
techniques reported in Jiang et al.10 and Trong et al 5. On the MR signal
change as functions of signal strength and phase, the experimental results were
reasonable considering RF accuracy because the values estimated by curve
fitting were also reasonable. The sensitivity threshold of SL-Mz was 2.34nT,
corresponding to magnetic field generated by about 75.07nAm dipole moment. SL-Mz
can measure big neural activity, but it’s desirable to improve sensitivity
threshold to measure smaller one. Then, it will be effective to improve SNR
using deep learning or signal processing.CONCLUSION
Simulation results showed that the field
strength generated from neural activites in the brain may the range from tens
to thousands pT. According to the experimental results, SL-Mz was more
sensitive to tiny oscillating magnetic fields than SIRS. However, SL-Mz in the low-field
MRI can detect 2.34nT magnetic field o equivalent to one generated by more than
75.07nAm due to its lower SNR. In our future works, we will apply noise-reduction
processing to improve SNR sufficient to detect small neural activities.Acknowledgements
This work was
partially supported by a Grant-in-Aid for Research (15H01813)
from the Ministry of Education, Culture, Sports, Science and Technology (MEXT),
Japan and a scholarship from the Iwadare Scholarship Foundation, Japan.References
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