Collin J. Harlan1,2, Keith A. Michel2,3, and James A. Bankson1,2
1The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States, 2Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3Imaging Physics Residency Program, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
Synopsis
Keywords: Hyperpolarized MR (Gas), Hyperpolarized MR (Gas), Transmit Gain Calibration, Bloch-Siegert Shift
Motivation: Transmit gain calibration is necessary for nonproton hyperpolarized MRI to accurately calibrate excitation angles.
Goal(s): To implement a phase-based TG calibration method using the Bloch-Siegert shift for nonproton HP MRI on a preclinical 7T scanner.
Approach: An off-resonance Fermi-shaped Bloch-Siegert pulse follows directly an excitation pulse. Percent difference between our measured Bloch-Siegert TG calibration factors and the validation TG calibration factors for 1H and 13C were calculated.
Results: Our method allows for fast, accurate, and robust TG calibration of preclinical nonproton HP MRI studies with minimal error (<5%) for 1H and 13C.
Impact: This method will facilitate efficient transmit
calibration for 13C and HP 129Xe MRI on a preclinical 7T
scanner, where limited bore size and the inability to easily implement a
thermal calibration phantom can make 129Xe TG calibration a
challenge.
Introduction
Transmit gain (TG) calibration is performed prior to
MR image acquisition to determine the radiofrequency (RF) output necessary to produce
desired excitation angles. Accurate TG
calibration is crucial for reproducible imaging of nonproton hyperpolarized
(HP) agents because each HP signal excitation attenuates the total available HP
signal pool, which directly effects future measurements.
We have previously implemented an
automated script to perform saturation-recovery
13C TG calibration on a preclinical 7T MRI for metabolic imaging of HP
[1-13C]-pyruvate in small animals. This approach leverages strong
signal from a heavily doped 8M 13C-urea phantom. While a thermal 129Xe
gas phantom was recently introduced for HP gas MRI1, this approach may
not easily translate into a small calibration phantom that can fit alongside small
animals in a 7T MRI.
We developed a TG calibration
sequence for nonproton HP MRI using the Bloch-Siegert shift2,3 in
preparation for upcoming HP 129Xe gas MRI small animal studies on a 7T
MRI. Our method allows for fast, accurate, and robust TG calibration of
nonproton HP MRI studies with minimal error (<5%) for 1H and 13C. Methods
Bloch-Siegert Effect: The Bloch-Siegert shift describes the
effect by which an off-resonant RF pulse induces a phase shift without altering
spin nutation of the target nuclei:
$$$\phi_{BS}=\int_{0}^{T}\frac{(\gamma B_{1}(t_{BS}))^{2}}{2(2\pi f_{BS})(t_{BS})}dt_{BS}\tag1$$$
Briefly, an
off-resonant RF pulse of duration tBS at offset frequency fBS
is applied following a single excitation pulse (Figure 1). Two repeated measurements
with ±fBS
allow for the quantification of the Bloch-Siegert phase difference, φBS, which is proportional to the square of
the amplitude of the RF field.
Imaging: All imaging
and spectroscopy were carried out using a 7T Biospec USR7030 small animal MR
scanner (Bruker Preclinical Imaging, Billerica, MA) and a dual-tuned 13C/1H
volume coil with a 35 mm inner diameter. An optimized Fermi shaped RF pulse4
was used for off-resonant Bloch-Siegert pulse generation. An analysis
script was implemented as a standalone MATLAB (The MathWorks, Natick, MA) executable
function that can be called automatically following data acquisition.
Phase Error Correction: During sequence development we
discovered a persistent phase error (φSwitch)
as a result of switching the frequency of the synthesizer to fBS, for the duration of the Bloch-Siegert pulse (tBS), between signal excitation and readout.
In order to isolate φSwitch, the Bloch-Siegert pulse was driven with 0 W (φBS = 0°) and tested at several fBS values ranging
0 to 2000 Hz at 1H (Figure 2). Other important pulse
parameters included tBS = 3.5 ms, TR = 10,000 ms, two repetitions, and two
dummy scans.
Phase induced by
switching the synthesizer frequency can be written as a function of the offset
frequency, pulse duration, and a minor delay associated with the frequency
switch:
$$$\phi_{Switch}=2\pi f_{BS}(t_{BS}+t_{Switch})\tag2$$$
The total phase (Δφ) observed following the Bloch-Siegert pulse reflects
a combination of these two effects:
$$$\triangle\phi=\phi_{BS}+\phi_{Switch}\tag3$$$
Bloch-Siegert
Validation: Following characterization of φSwitch, the Bloch-Siegert pulse was driven with power values
ranging 0 to 20 W at 1H (Figure 3) and 13C. Measured Δφ values were corrected
for phase induced by the frequency switch. Next, φBS was used to calculate TG calibration factors
at each Bloch-Siegert power level. Finally, percent difference between our
measured Bloch-Siegert TG calibration factors and the validation TG calibration
factors for 1H and 13C, measured automatically by the scanner and using our existing saturation-recovery
13C TG calibration method, were calculated. Results
φSwitch was measured to be ~7° when tBS = 3.5 ms, fBS = 2000
Hz and tSwitch was calculated to be ~10 µS.
Figure 4 provides a summary of percent difference between our measured
Bloch-Siegert TG calibration factors and the validation TG calibration factors
for 1H and 13C at power levels ranging 0 to 20 W. The reference power required for the scanner to generate a 90°, 1 ms
hard excitation pulse was measured to be 3.05 W and 11.69 W for 1H
and 13C, respectively. Discussion and Conclusion
We successfully utilized the
Bloch-Siegert effect to develop a robust pulse sequence that can be used for
fast and automated TG calibration of nonproton nuclei on a 7T small animal MRI
with minimal error (<5%). Our method performs well for 1H and 13C.
We are currently in the process of testing our method using HP 129Xe
gas and a dual-tuned 129Xe/1H
volume coil. Our Bloch-Siegert pulse and supporting developmental tools can
be found at: https://github.com/mda-mrsl/PV-BSXmit Acknowledgements
This work was supported by funding from
the National Cancer Institute of the National Institutes of Health
(R21CA280799, T32CA196561). The content is solely the responsibility of the
authors and does not necessarily represent the official views of the sponsors.References
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