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Susceptibility artifact correction in MR thermometry for monitoring of mild RF hyperthermia using total field inversion

Christof Boehm¹, Marianne Goeger-Neff², Hendrik T. Mulder³, Benjamin Zilles², Lars H. Lindner², Gerard C. van Rhoon³, Dimitrios C. Karampinos⁴, and Mingming Wu¹
¹Technical University of Munich, Munich, Germany, ²Department of Medicine III, University Hospital, LMU Munich, Munich, Germany, ³Erasmus MC Cancer Institute, Rotterdam, Netherlands, ⁴Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany

Synopsis

Motion-induced susceptibility changes induce field variations, leading to large errors during MR thermometry based on the linear proton resonance frequency shift. These artefacts aggravate temperature quantification in the face of both the long treatment duration and the mild temperature change during mild RF hyperthermia treatments. We show with the help of simulations, a phantom heating experiment, volunteer scans and mild hyperthermia treatment of a patient with cervical cancer and a sarcoma patient how to correct for this artefact source by methods known from quantitative susceptibility mapping. The recently introduced total field inversion shows advantages over the background field removal methods.

Purpose

Mild heating of various cancer types sensitizes tumors to radio- and chemotherapy¹. This treatment modality has thus found its way into clinical practice in recent years, including the treatment of sarcoma patients and patients with cervical cancer^2-5. MR temperature monitoring of mild hyperthermia (HT) treatment (40-44°C) of cancer is done by exploiting the linear resonance frequency of water^6-8. However, susceptibility distribution changes between different time points, due to digestive motion including gas or movement of air bubbles inside the circulating water bolus create much stronger phase changes than the temperature-induced phase change of -0.01ppm/°C⁹. Recently, it was proposed to correct for the susceptibility artifacts by solving the Laplacian boundary value problem (LBV)¹⁰ or by projection onto dipole (PDF)^11,12. These two methods are well-known in the context of quantitative susceptibility mapping for separating the foreground from the background fields. However, PDF is known to overfit especially at air-tissue interfaces and Laplacian-based method reduce the field of view. Total field inversion (TFI)¹³ has been proposed to alleviate the above problems and is presently applied.

Theory

To estimate the phase contributions originating from susceptibility sources the following TFI cost function is solved similar to¹³:$$y'=\underset{y}{\arg\min}=\frac{1}{2}||w(f-d*Py)||_1+\lambda{||}\nabla{P}y||_1,$$where$$$\:{w}\:$$$the a data weighting term,$$$\:{f}\:$$$is the input phase,$$$\:{d}\:$$$is the dipole kerne in$$$\:{k}\:$$$-space,$$$\:{P}\:$$$the preconditioner and$$$\:\nabla\:$$$the gradient operation. The final susceptibility distribution is computed as$$$\:\chi=Py$$$.

Methods

The phantom heating experiment as well as the volunteer data set were acquired on a 1.5T GE system (GE Discovery MR450w/USA). The BSD2000-3D Sigma Eye MR-compatible RF applicator (PYREXAR Medical/USA) consists of 24 dipole antennas arranged in three rings of 8 antennas. No water was circulated during the phantom measurement or the volunteer study. For phase unwrapping, we used the code available at (https://gitlab.com/veronique_fortier/Quality_guided_unwrapping).
Simulations: The simulated matrix size was$$$\:150\times150\times150$$$, which corresponded to a FOV of$$$\:50\times50\times50cm^3$$$. The simulated field disturbance originated from a susceptibility change $$$\Delta\chi\:$$$within a sphere of$$$\:2\:$$$cm diameter in the center. The susceptibility difference $$$\Delta\chi$$$ was the one between water and air. A 3D Gaussian temperature increase profile with a peak value of 10°C and a standard deviation of$$$\:5\:$$$pixels was added to the image. A spatially variant 1st order phase was added to imitate B0 drift.
Phantom heating: A double echo GRE with slice interleaved acquisition scheme was scanned for temperature monitoring. Using the phase signal at both TEs compensated for conductivity change-induced phase offsets (7) (TR=620$$$\:$$$ms, 25 slices, scan time=83 s, TE_1=4.8 ms, TE_2=19.1 ms, matrix size=$$$128\times128$$$, FOV=50 cm$$$\times\:$$$50 cm, flip angle=40°, slice thickness=10 mm, bandwidth=325.5 Hz/px.)
Volunteers: Single echo data sets were acquired for in total 4 volunteer data sets (3 male, 1 female) (TE/TR=15ms/21ms, 20 slices matrix size=$$$128\times160$$$, FOV=50 cm$$$\times\:$$$50 cm, flip angle=14°, slice thickness=10mm). As$$$\:$$$at constant temperature, the conductivity bias did not need to be considered, a single echo$$$\:$$$acquisition scheme was$$$\:$$$sufficient. 30 min passed between the two acquisition time points.
Patient treatment: Patient treatment$$$\:$$$scans were performed with the$$$\:$$$approval of the respective local ethics board. A Double-Echo Gradient Echo (DEGRE) acquisition corrected for the conductivity bias¹⁴. We evaluated the mild RF-HT of a cervical tumor treated$$$\:$$$with the aforementioned BSD2000-3D Sigma Eye applicator inside a$$$\:$$$1.5T GE system. Furthermore, we evaluated the mild RF-HT of a sarcoma in the thigh, which was treated with the more novel BSD-2000 3D/MR applicator inside a 1.5T Philips system.

Results

Simulations: All three methods can eliminate both the linear phase as well as the dipole, while preserving the simulated heat distribution. While the LBV method performed best, PDF and TFI performed similarly, as seen in$$$\:$$$the cumulative error plot(Fig.1).
Phantom$$$\:$$$heating: The PDF algorithm overestimates the background field effect particularly at the border of the phantom(black arrow in Fig.2). Furthermore, we could see that all three$$$\:$$$methods successfully removed the$$$\:$$$B0 drift effect and matched well to the sensor probe data.
Volunteers: In contrast to the simulation results, here, TFI showed the best performance in terms of$$$\:$$$cumulative error in all 4 volunteers(Fig.3).
Patient treatment: The advantage of preserving pixel layers becomes particularly apparent in case of tumors in proximity to tissue/air interfaces, as$$$\:$$$for the case$$$\:$$$of cervical cancer treatments with MR thermometry monitoring(Fig.4). It is important to note that the severity and spatial extension of the susceptibility artifacts would not allow for B0 drift correction, as the silicon tubes would be superimposed by that same artifact. Results for a sarcoma patient treatment are shown(Fig.5).

Discussion

TFI performs more robustly in the presence of noise, as seen in the phantom and volunteer measurements. This is a big advantagein the context of RF-HT treatments, as the heating device precludes the use of other MR receive coils than the body coil and thus suffers from low SNR. In contrast to LBV, all pixels are preserved. This is particularly useful as the tumor is oftentimes located next to intestinal gas, and thus at the edge of the foreground mask.

Conclusion

LBV, PDF, and TFI successfully remove susceptibility artifacts without subtracting the phase change due to temperature change. The B0 drift correction comes for free. The PDF algorithm has the tendency to overestimate the background field contribution, whereas the LBV method peels valuable pixel layers away for fitting. Thus, TFI might be a promising method for gaining accurate temperature maps during mild RF-HT treatments.

Acknowledgements

No acknowledgement found.

References

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Figures

Figure 1: Simulated field disturbance and the impact of the investigated background field removal methods. In addition to the dipole which was created by simulating a susceptibility change inside the sphere, a spatially varying linear phase was added to simulate B0 drift effects.

Figure 2: Temperature change maps from a gel phantom heating experiment are shown. The black arrow points at the border of the phantom, where PDF has overestimated the impact of the background field and thus subtracted more phase than the competing methods. The results after correction are matched to a ROI around reference sensor tips and correspond well to the probe data.

Figure 3: Bowel motion-induced susceptibility artefacts in volunteers at constant temperature and their correction with LBV, PDF and TFI. The temperature error maps before and after correction are displayed. In the cumulative error plots, the ratio is calculated between the numbers of voxels occupying the given value and less over all voxel counts. In contrast to the simulation results, as seen in Fig.1, the cumulative error plots (last row) indicate that TFI results in the least residual phase errors.

Figure 4: Resulting temperature maps for a cervical cancer patient during mild RF-HT of the tumor. Particularly for tumors close to the intestines, gas motion causes severe artifacts. The black arrows point at residual phase errors after background field correction, that appears to be less in the TFI. Furthermore, the LBV method resulted in the loss of valuable pixels. The comparison of the corrected temperature with a sensor illustrates how severe the susceptibility artifacts were in the uncorrected DEGRE.

Figure 5: Results from the mild RF-HT treatment of a sarcoma in the right thigh. The heat cumulation in the water bag between the thighs becomes visible after applying PDF or TFI, but the LBV method had removed these pixel layers. The B0 drift effect in the uncorrected temperature map is removed by all three methods. Matching the temperature sensor to a ROI around the catheter revealed a mild heating after susceptibility correction. Intestinal gas motion also affect the signal within the thighs as the susceptibility artefacts propagate as dipole fields.

Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)

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