Junichi Tokuda1, Kemal Tuncali1, Lisanne Kok1,2, Vincent M Levesque 1, Ravi T Seethamraju 3, Clare M Tempany1, and Ehud J Schmidt1
1Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States, 2Eindhoven University of Technology, Eindhoven, Netherlands, 3Siemens Healthcare, Boston, MA, United States
Synopsis
We tested the feasibility of R2*-based
temperature mapping using a PETRA UTE sequence to determine the “kill zone” within
an ice ball in the kidney during MRI-guided renal cryoablation. R2*-maps
were calculated from dual-echo PETRA images acquired during six renal cryoablation
cases, and converted to temperature maps using R2*-temperature calibrations
performed in swine kidneys. We compared ablation volumes estimated from (a) the
-20°C boundary on the temperature maps; (b) the signal void on intra-procedural
T2-weighted images; and (c) post-ablation contrast-enhanced MRI as the “gold
standard”. Results show that R2*-based temperature maps provided a reliable
lower limit of the kill-zone volume.Purpose
Percutaneous kidney cryoablation has emerged as an option
for renal tumor treatment that avoids surgical morbidity and complications [1].
MRI is an ideal tool to monitor ablation margins in cryoablation, because it
can visualize both the tumor and the ice ball with greater accuracy than CT and
ultrasound [2,3]. However, the ice ball seen as a signal void on ordinary MRI sequences
(with TE>1ms) does not represent the ablation volume accurately; studies
have shown that the critical temperature to induce malignant cell necrosis is -20°C
[4], while MRI can only delineate the boundary of frozen tissue. To address
this issue, MRI-based temperature mapping using ultrashort TE (UTE) imaging was
proposed [5-7]. In this study, we tested the feasibility of R2*-based
temperature mapping using a 3D UTE Point-wise Encoding Time Reduction with radial
Acquisition (PETRA) sequence [8] to determine the volume of tissue ablated or “kill
zone” created by cryoablation in the kidneys, using a post-ablation
contrast-enhanced MR (CE-MRI) as the “gold standard”.
Methods
Ex Vivo Swine Kidney Experiment
for Calibration. The relationship between the R2* change and temperature
was determined using two ex vivo swine kidneys. Two 17-gauge stainless-steel MRI-compatible
cryoablation probes (Galil Medical) were inserted into each kidney with two
thermocouples (Omega) embedded in carbon-fiber needles for reference
temperature measurement. Cryoablation was performed over 30 minutes in a 3T MRI
Scanner (Verio 3T, Siemens) using an MRI-compatible cryoablation system (Galil
Medical), while the temperatures at the thermocouple tips were recorded. MR
images were continuously acquired using a dual-echo PETRA sequence (TR/TE1/TE2=4.5/0.07/2.0ms;
matrix=160×160×160; pixel size=2mm3; flip angle=8°; FOV=350mm3;
11000 spokes, TA=1min/vol). R2* maps were computed by fitting the
first and second echoes to a decaying exponential. In order to cancel
inhomogeneous R2* close to the cryoablation probes, a ΔR2*
map was calculated by subtracting a baseline (25 °C) R2* map from each
R2* maps. The mean ΔR2* within a region of interest of ~300 mm3
at each thermocouple tip was correlated with the reference temperature. A
linear least-square fit between ΔR2* and temperature was performed.
Data Acquisition in MRI-guided Renal Cryoablation.
The Institutional Review Board approved this study. MR images were acquired
in six patients during MRI-guided renal focal tumor cryoablation with two
freeze-thaw cycles. Patients were treated under monitored anesthesia care (MAC)
(n=5) or general anesthesia (GA) (n=1). PETRA images were acquired at baseline and
between regular T2-weighted scans with a 2D half-Fourier acquisition
single-shot turbo spin echo sequence (TR/TE=1000/200ms; matrix=320×190;
FOV=289×340mm2; slice=3.7mm) during a 15-minute freezing cycle. During
the scan, the patient was instructed to breath-hold (MAC), or was under
controlled apnea (GA).
Data
Analysis. After computing ΔR2*
maps, a temperature map within the ice ball at the end of the second freezing
cycle (15-min) was estimated using the calibrated parameters. The ablation
volume was estimated using: (a) the -20°C boundary on the temperature map (R2*Temp
volume); (b) the signal void on the intraoperative T2-weighted image (T2w
volume); and (c) the hypo-intense, non-perfused area on a CE-MRI (Post CE
volume) acquired 24 hours after the procedure.
Results
The
ΔR2*-to-temperature calibration provided the equation
ΔR2*=0.00633×
T+0.0628, where
T is the
temperature in °C. Fig. 1 shows representative temperature maps, T2-weighted
MRI, and follow-up CE-MRI at corresponding slices. The estimated ablation
volumes using (a)-(c) are shown in Table 1. Overall, the temperature map showed
smaller ablation volumes relative to the T2-weighted MRI (
p<0.05). The
temperature map underestimated the ablation volume in 5 cases, whereas
T2-weighted MRI overestimated in 4 cases, but the differences were not
significant (
p=0.16 and 0.06).
Discussion and Conclusion
The R2*-based temperature maps appear to provide a reliable
lower limit on the extent of the kill zone, but there are inconsistent results
when comparing R2*Temp and T2w volumes. It is likely that the kill zone is larger
than the thermal estimate, since the mechanisms of cell death involve various
factors such as microvasculature thrombosis, the number of freeze-thaw cycles,
and the rate of thawing. R2*-based temperature maps contained artifact due to large
streaking artifacts (case 3) and mis-registration due to breathing or gross
motion (cases 1 and 6; both under MAC), which might have contributed to under-/overestimation
of the ablation volume. The streaking artifact resulted from the use of fewer radial
spokes (11000 vs. 25000 for minimal-streaking) as required to maintain reasonable
breath-hold durations. Accurate image registration may improve these results. The
current analysis was limited to a single time point, and ignored time-increment
freeze-thaw cycles and voxels that became the critical temperature before
15-min. We are currently analyzing the other time points in an effort to tighten
the lower bound.
Acknowledgements
NIH P41EB015898 supported this study. Siemens Healthcare
provided PETRA. We thank Janice Fairhurst, R.T. for all MR image
acquisition.References
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