Henrik Odéen1, Scott Almquist2, Joshua de Bever1, and Dennis L Parker1
1Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, United States, 2School of Computing, University of Utah, Salt Lake City, UT, United States
Synopsis
Thermal
model based reconstruction of subsampled MR temperature data for focused
ultrasound applications rely on acoustic and thermal parameters that are often analytically
determined from a pre-treatment sonication. In this work we combine a thermal
model based reconstruction method with ultrasound beam simulations to determine
the specific absorption rate in order to avoid potentially damaging the tissue
during a pre-treatment sonication. Proof-of-concept experiments are performed
in a homogenous gelatin phantom and a gelatin phantom embedded with a plastic
skull. The temperature estimations using US modeling show the same accuracy as
those using a pre-treatment sonication.Introduction/Purpose
MRI
has become the imaging modality of choice for monitoring focused ultrasound
treatments due to its ability to combine good soft tissue contrast with temperature
measurements obtained using MR temperature imaging (MRTI). One current
challenge in MRTI is being able to combine high spatio-temporal resolution
imaging, to ensure accurate temperature measurements, with large field-of-view,
to be able to monitor any unwanted US power deposition occurring in the US
near- and far-field. To achieve increased acquisition speed, which in turn can
be traded for higher resolution and larger field-of-view, various compressed-sensing
and model
based reconstruction methods of subsampled k-space data have been
described.
In
this work we combine a previously described thermal-model-based reconstruction
method for subsampled MRTI data called model predictive filtering (MPF)1 with US beam modeling
using the hybrid angular spectrum (HAS) method2. The thermal and acoustic parameters used in MPF have
previously been derived using an analytical model fit3,4 to a pre-treatment, low power sonication. Utilizing
US modeling to derive the specific absorption rate (SAR) can avoid the need for
this potentially tissue damaging sonication.
Methods
In
MPF, temperature maps for time-frame (n+1)
are calculated by combining subsampled k-space data with a forward-prediction
of the temperature distribution from time-frame (n) using the Pennes Bioheat Transfer equation. After combination and projection into image space, temperatures
are calculated from the image phase using the proton resonance frequency shift
method.
In
this proof-of-concept study, experiments were performed in a homogenous gelatin
phantom5 as well as in a gelatin phantom with an embedded
homogenous PVC plastic skull, Figure 1. For the skull phantom experiment, US
phase aberration correction was simultaneously performed using HAS6. MRTI reconstructions were performed with MPF using
HAS with no pretreatment heating and using analytically derived SAR and thermal
conductivity from a low power pre-treatment heating. The analytical solution
uses tabular values for density and specific heat, whereas MPF with HAS uses
tabular values for all tissue properties. MPF reconstructions were compared to
a temporally constrained reconstruction (TCR) method7 as well as to fully
sampled “truth”.
All
MR imaging was performed using a 3D segmented EPI pulse sequence on a 3T MRI scanner
(TIM Trio, Siemens, Erlangen, Germany) with imaging parameters summarized in
Table 1. k-space was subsampled by varying the sampling density in the kz-slice
encode direction to achieve denser sampling over the k-space center, while
keeping the spacing between the echoes down the echo train equal for all echoes8. FUS heatings were performed with an MR-compatible
phased-array US transducer (256 elements, 1 MHz frequency, 13 cm radius of
curvature, 2x2x8 mm focal spot FWHM, Imasonic (Besancon, France) and Image
Guided Therapy (Bordeaux, France)). In the homogenous phantom FUS sonications
were performed at 40 W for 28.7 s (subsampling and “truth”) and at 14 W for
28.7 s (for analytical property determination),
and in the skull phantom at 125 W for 23.9 s (subsampling and “truth”) and at
100 W for 14.3 s (analytical property determination). The high-power sonications
were repeated three times.
Results
Figure
2 shows the temperature evolution of the hottest focal spot voxel versus time for
the FUS sonications in the two phantoms, as well as the error compared to “truth”.
The mean and standard deviation (SD) of the root mean square error for MPF HAS,
MPF analytical, and TCR in the homogenous phantom was 0.61±0.02, 0.78±0.02, and
0.54±0.21 °C, respectively, and in the skull phantom 0.45±0.09,
0.40±0.01 and 0.32±0.07 °C, respectively. The spatial
temperature distribution for “truth”, and the spatial error of both MPF and the
TCR reconstructions, are shown in Figure 3. In Figure 4 three orthogonal views
of the estimated SAR patterns are shown. In the skull phantom the large FOV
temperature maps detected an approximately 8 °C temperature rise in the near-field
on the “brain” surface, and an 8% increase of max temperature rise when using the
aberration correction (data not shown).
Discussion
and Conclusions
The
results show that it is possible to utilize US modeling to predict the SAR
pattern in both homogenous phantoms and when focusing through a plastic skull,
and in turn use the modeled SAR pattern in MPF to achieve temperature maps with
accuracy similar to MPF using analytically derived SAR and TCR. The analytical
SAR estimate was higher and wider than the HAS estimate, but the accuracy in the
MPF temperature maps were similar, highlighting the robustness of MPF in
general. By utilizing US modeling in conjunction with MPF the need for a
potentially tissue damaging pre-treatment sonication can be avoided.
Acknowledgements
This work was supported by The Focused Ultrasound Surgery Foundation,
Siemens Healthcare, The Ben B. and Iris M. Margolis Foundation, and NIH
grants R01s EB013433 and CA134599References
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