Inter-laboratory study of a computational radiofrequency coil model at 64 MHz
Elena Lucano1,2, Mikhail Kozlov3, Eugenia Cabot4, Sara Louie5, Marc Horner5, Wolfgang Kainz1, Gonzalo G Mendoza1, Aiping Yao4,6, Earl Zastrow4,6, Niels Kuster4,6, and Leonardo M Angelone1

1Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, U.S. Food and Drug Administration, Silver Spring, MD, United States, 2Department of Information Engineering, Electronics and Telecommunications, University of Rome "Sapienza", Rome, Italy, 3MR:comp GmbH, Gelsenkirchen, Germany, 4IT'IS foundation, Zurich, Switzerland, 5ANSYS, Inc., Canonsburg, PA, United States, 6Department of Information Technology and Electrical Engineering, ETH, Zurich, Switzerland

Synopsis

Preliminary results of an ongoing inter-laboratory study are presented. The eventual purpose of the effort is to develop a methodology that harmonizes RF-modeling and RF-testing protocol for use in RF exposure assessment. In this phase of the study, numerical and experimental data were collected from four laboratories for unloaded and loaded coil conditions. Only information about the geometry and resonance frequency of the physical coil was provided. Qualitatively good agreement across all teams was found. Subsequent phases of the study shall include a methodology on uncertainty analysis associated with the numerical and experimental methods that can be used in practice

Purpose

Several electromagnetic (EM) simulation platforms have been used in literature to calculate the EM field generated by a magnetic resonance imaging (MRI) RF coil. We present preliminary results of an ongoing inter-laboratory study that aims to compare the numerical data obtained by users of different software platforms modeling a commercially available RF coil, i.e., the MITS system. The computational data was then compared to measurements obtained using two MITSs systems in two different laboratories.

Methods

The Medical Implant Test System (MITS1.5) for 1.5 Tesla RF Safety Evaluation (Zurich Med Tech) (Figure 1a) consists of a 16 rung high-pass birdcage coil (inner diameter = 740 mm, length = 650 mm, Figure 1c) [1]. The coil is shielded by a metallic enclosure and is driven at two ports physically shifted by 90o (I and Q in Figure1a). A simplified geometric model of the MITS1.5 coil (Figure 1b) was distributed in CAD format to four teams: FDA (Team 1), IT’IS (Team 2), MRCOMP (Team 3), and ANSYS (Team 4). No further information on the matching and tuning network, and the EM fields from the physical coils were provided. EM simulations were conducted independently with three software platforms: xFDTD (Remcom Inc.) (Team 1), SEMCAD-X (Schmid & Partner Engineering) (Team 2), both of which employed the finite difference time domain method, and HFSS (ANSYS Inc.), which employed finite element method (Teams 3 and 4). The EM fields were measured with the DASY52 NEO robotic measurement system (SPEAG) [2]. In the first step, numerical and measured data were collected for coil unloaded and loaded with ASTM phantom (Figure 1d). Metallic components were simulated as either copper or perfect electric conductor. The coil model was tuned at 64MHz. Numerical and measured data were reported for axial and coronal planes for the unloaded condition (Figure 2a), and for coronal planes for the loaded condition (Figure 2b). For a quantitative comparison, all results were rescaled to the norm of the magnetic field vector at the center of the RF-coil (|H_iso|). Numerical and measured data were compared using the SMAPE error approach [3].

Results

Figure 3 and 4 show comparison of the numerical and measured electric field in both loading conditions obtained by each team (i.e., one for each quadrant). Herein results are compared only with one of the two measured datasets. Each point in the figure is drawn with the coordinates based on the simulated value in the x-axis and the same measured value in the y-axis. The closer the distribution is to the diagonal, the closer the numerical data are to the measurements (1:1 line). Conversely, a point closer to the ordinate (i.e., measure axis) is representative of a higher measured value with respect to the simulated one, and vice versa. Point distributions were similar for all teams, with an overall better data match for the coronal planes and the axial planes far from the isocenter. Results for the central axial plane (i.e. plane C in Figure 3) were affected by the low electric field values. Figure 5 shows the magnetic and electric field distributions inside the ASTM phantom. The measured magnetic field distribution was more asymmetric than the simulated one. Overall, good agreement was found for the electric field distribution. The mean SMAPE [3] (see equation in Figure 5) inside the plane was equal to 15.7%, 19.5%, 34.71% for Teams 1, 2 and 3, respectively. Consolidation for results from Team 4 is still pending. SMAPE variations may be caused by large sensitivity of |H_iso| on magnetic field symmetry.

Discussion and Conclusions

Further analysis will be conducted to find reasons for the magnetic field asymmetry around the iso-center. Figure 4c suggests that the correlation between numerical and measured data for all teams may be affected by the asymmetry of the magnetic field. Additional post-processing will be performed to take such asymmetry into account. Further steps of the project will include simulation of MITS guided by additional MITS information: coil S-parameters and field distribution. The results from all software platforms will be compared, and uncertainty levels will be defined between the measurements datasets. This will allow a complete comparison including different level of accuracy of the field distributions and power requirements for both numerical and physical coils.

The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services

Acknowledgements

No acknowledgement found.

References

[1] Lucano et al., ISMRM (2014), 4903

[2] DASY 5NEO, SPEAG, Zurich, Switzerland

[3] J. S. Armstrong, Long-Range Forecasting: From Crystal Ball to Computer: Wiley 1985

Figures

Figure 1: (a) MITS1.5 physical coil. (b) 3D view of the computational model. (c) dimensions of the generic model used, and (d) dimensions of the ASTM phantom used as a symmetrical load inside the coil.

Figure 2: Dimensions of the measured planes inside the coil. (a) axial and coronal planes in the unloaded condition, and (b) coronal planes with the coil symmetrically loaded with the ASTM phantom, one internal and two external coronal planes.

Figure 3: one-by-one comparison in unloaded condition for the electric field rescaled to the norm of the magnetic field at the isocenter (V/m)/(A/m)). A-E coronal planes, F-J axial planes. On the left side legend is reported: each quadrant shows comparison for a specific team (1 to 4).

Figure 4: one-by-one comparison in loaded condition for the electric field rescaled to the norm of the magnetic field at the isocenter (V/m)/(A/m)). A and B coronal planes outside the ASTM phantom, C central coronal plane inside the ASTM phantom. Same legend is followed as in figure 3.

Figure 5: EM field distribution inside the ASTM phantom for the central coronal plane: measure data, simulated data from the four teams (i.e., Team1, Team2, Team3), and SMAPE distribution inside the plane. White area in the distribution denotes unmeasured values due to DASY system constrains.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
0356