Christoph Stefan Aigner1, Armin Rund2, Samy Abo Seada3, Shaihan Malik3, Joseph V Hajnal3, Karl Kunisch2,4, and Rudolf Stollberger1
1Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 2Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria, 3Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom, 4Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, Linz, Austria
Synopsis
We demonstrate the joint design of minimum
duration RF and slice selective gradient shapes with included gradient impulse
response function (GIRF) to correct for non-ideal slice selective gradients in
the pulse design. The proposed time-optimal control method designs slice
selective gradient shapes with matched RF shapes for the distorted slice
selective gradient. Phantom experiments on a 3T scanner validate the optimized
results and demonstrate the successful correction of gradient imperfections
with the proposed approach.
Purpose
Joint design of minimum duration RF and slice
selective gradient (Gs) shapes by time-optimal control1 for Simultaneous
Multislice (SMS)2 refocusing can reduce the pulse length
significantly3. Optimal solutions fully exploit the prescribed
inequality constraints (slice profile accuracy, amplitude and phase
constraints), which tends to produce strongly varying Gs shapes3.
This might prevent the practical application of such optimized pulses due to
gradient imperfections caused by eddy currents or time delays, which alter the oscillating
Gs shapes. The gradient performance can be modeled by the time-invariant gradient
impulse response function (GIRF)4 and its effect can be iteratively
corrected for a given RF and Gs shape5. In this work, we show that a
direct inclusion of the GIRF in a time optimal control framework1 allows
to design short RF and Gs shapes for SMS refocusing that inherently corrects
for non-ideal gradients.Theory
A time optimal control framework to jointly
design the RF and Gs shape based on the Bloch spin domain formulation of a
perfectly crushed spin echo $$$|b(z)|^2$$$ is introduced. The effects of the
GIRF are modeled by a convolution in the time domain $$Gs_{GIRF}(t)= Gs(t)*GIRF(t).$$ Amplitude
constraints for RF, Gs and Gs slew rate (for $$$Gs(t)$$$) and a global RF power
constraint are prescribed. The Bloch simulations and slice profile constraints
(magnitude and phase) are based on the filtered GsGIRF. We apply
projection-based techniques (semismooth Newton) for the box constraints of the
controls, and penalty techniques for the more general constraints. Adjoint-based
exact first derivatives are formed via the Lagrangian, the second derivatives
are supplied by a quasi-Newton (BFGS)6 algorithm. The free terminal
time is optimized with the bilevel method for time-optimal control of our preceding
work1.Methods
The iterative optimization
started with a PINS7 RF and slice selective gradient shape based on
a SLR8 sub-pulse. An example with five slices and real-valued RF
pulses is presented. The maximal allowed error deviation of the individual
refocused slices (thickness=2mm, time-bandwidth-product=4, slice-gap=24mm) from ideal refocusing was set
to pointwise 2% (out-of-slice) and 3% (in-slice). The discretization used a
spatial domain of 120mm with 2401 spatial points and a temporal discretization
of 6.4µs. The constraints were B1max=13µT, Gsmax=39mT/m
and slew ratemax=180mT/m/ms, respectively. The GIRF(t) was measured
for a longitudinal (z-) gradient for a Philips Achieva 3T system using an
image-based procedure. The proposed algorithm is implemented in MATLAB (The
MathWorks, Inc, Natick, USA) and the calculations are done in parallel (OpenMP)
using 16 cores and 128GB of RAM (Radon1, RICAM, Linz, Austria). The designed refocusing
pulses were validated on a phantom by measuring refocusing profiles in a spin-echo
sequence, using conventional MB excitation pulses with matching slice profiles.
Experiments were conducted on the same system and gradient axis for which the
GIRF was measured.Results and Discussion
Figure 1 shows the PINS initial (left) together
with the optimized results using no GIRF (middle) and the measured GIRF (right).
The overall duration of the optimized results is reduced from 18.58ms to 3.17ms
and 3.6ms, i.e. the inclusion of the measured GIRF in the optimization
increases the optimized pulse duration only slightly and the optimized shapes
of RF and Gs remain similar. Both, the RF amplitude and the slew rate of Gs meet
the prescribed constraints. Figure 2 shows Gs and GsGIRF with and
without inclusion of the GIRF into the optimization. Depicted are also the simulated
refocusing profiles with and without GIRF. Gradient imperfections associated
with high frequency components result in distorted slice profiles, most
noticeable in the outer slices of the pack. The inclusion of the measured GIRF
in the optimization however, completely corrects for these unwanted slice
profile distortions and yields distinct refocusing slices across the field of
view. While the optimizer generates a pre-emphasized Gs shape with frequency
components outside the available amplifier bandwidth, the RF pulse and GsGIRF
shape are matched to yield precise slice profiles. Figure 3 shows the refocusing profile for a B0 offset range of
±200Hz (left side) and a B1 variation of 85-125% for the GIRF optimized RF
pulse and GsGIRF shown
in Figure 2. These robustness properties agree with those of minimum-duration
pulses without GIRF1. Figure 4 shows the reconstructed phantom
measurements using the optimized RF and Gs shapes shown in Figure 1. As can be
seen, the inclusion of the GIRF in the optimization clearly improves the refocused
slice profile.Conclusion
The inclusion of a
hardware specific GIRF into the optimal control framework allows to compensate
for the gradient imperfections and achieve distinct refocusing slices, even for
minimum-duration RF and Gs shapes. Acknowledgements
Partly supported by the Austrian Science Fund
(FWF): SFB F3209-18 and BioTechMed-Graz. Samy Abo Seada is funded by the King’s College London &
Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging
(EP/L015226/1).References
1) Rund A, Aigner CS, Kunisch K and
Stollberger R. Simultaneous multislice refocusing by time-optimal
control. Proc. Intl. Soc. Mag. Reson. Med. 25, 0522,
Honolulu, Hawai'i, USA, 2017.
2) Barth M, Breuer F, Koopmans PJ, Norris
DG and Poser BA. Simultaneous multislice (SMS) imaging techniques. Magn. Reson.
Med. 75(1):63–81, 2016.
3) Grissom WA, Setsompop K, Hurley SA, Tsao
J, Velikina JV and Samsonov AA. Advancing RF pulse design using an
open-competition format: Report from the 2015 ISMRM challenge. Magn. Reson.
Med. 78(4):1352–1361, 2016.
4) Vannesjo
SJ, Haeberlin M, Kasper L, Pavan M, Wilm BJ, Barmet C, Pruessmann KP. Gradient system characterization by impulse response measurements
with a dynamic field camera. Magn. Reson. Med. 69(2):583–593, 2013.
5) Abo Seada S, Hajnal J, and Malik
S. A simple optimization approach to making time efficient VERSE-multiband
pulses feasible on non-ideal gradients. Proc. Intl. Soc. Mag. Reson. Med. 25,
5049, Honolulu, Hawai'i, USA, 2017.
6) Nocedal J and Wright SJ. Numerical
Optimization. Springer Series in Operations Research and Financial Engineering,
Mikosch TV, Resnick SI, and Robinson SM, Eds. New York: Springer, 2nd
edition, 2006.
7) Norris DG, Koopmans
PJ, Boyacioglu R and Barth M. Power Independent of Number of
Slices (PINS) radiofrequency pulses for low-power simultaneous multislice
excitation. Magn. Reson. Med. 66(5):1234–1240, 2011.
8) Pauly J, Le Roux P, Nishimura D, and
Macovski A. Parameter relations for the Shinnar-Le Roux selective excitation pulse
design algorithm. IEEE Trans. Med. Imaging 10(1), 1991.