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Data Analysis of Report
Published in Kitsakorn Locharoenrat, Research Methodologies for Beginners, 2017
Since the Maxwell–Garnett model cannot reproduce the systematic changes in the polarization dependences of absorption intensity qualitatively, we suggest that this model is not appropriate for predicting our absorption spectra. In order to remove the first and second sources of discrepancies mentioned above, rigorous numerical studies using the finite-difference time-domain method of solving the electromagnetic problems by integrating Maxwell’s differential equations are under way. This method will also allow us to consider the actual wire size dependence of the peak energy positions.
Validation and practical use of Plan2Heat hyperthermia treatment planning for capacitive heating
Published in International Journal of Hyperthermia, 2022
Because of the low frequency applied for capacitive heating, power distributions were calculated by solving the quasi-static formulation of Maxwell’s equations, enabling much faster simulations compared to solving the full Maxwell’s equations using the Finite Difference Time Domain method. The metal electrodes at the top and bottom were kept at a constant potential of 1 V and −1 V, respectively. The boundaries of the simulation domain were fixed at zero potential. The electric field vector can be written as V [20]. Plan2Heat uses CUDA GPU accelerated calculations. The power density (PD) is then calculated using: m−1) the electrical conductivity. For SAR evaluation 1 cc averaged values were considered. Dielectric and thermal tissue properties used in the phantom and patient simulations were based on literature and are listed in Table 1.
Effect of gastrointestinal gas on the temperature distribution in pancreatic cancer hyperthermia treatment planning
Published in International Journal of Hyperthermia, 2021
Astrid van der Horst, H. Petra Kok, Johannes Crezee
For the calculations, executed in Plan2Heat, each patient volume (sCT, sCT0 and each of the eight sCTCBCT was resampled to 2.5 × 2.5 × 2.5 mm3; for remaining artifacts, for example from lead positioning markers, voxels were assigned an appropriate segmentation (e.g., external air or fat, depending on whether the voxel was located inside or outside the patient contour). Next, the patient anatomy was positioned between the antennas such that the CTV was located centrally in front of the waveguide apertures in cranial-caudal direction. The CT scans were 23.75–29.75 cm in cranial-caudal direction. This is insufficient for accurate electromagnetic field simulations, since the water bolus would extend beyond the patient model, thereby introducing artifacts by introducing incorrect fringing fields at the cranial and caudal borders of the computational domain. Therefore, for each patient the volume was extended to 50 cm by repeating the most cranial and most caudal slices [47]. Although this approach introduces errors in the anatomical information and computed fields in the border regions, the effect on the field distribution in the central tumor target region will be minimal; in addition, with an ALBA-4D focus size of 15–20 cm [45,46], field values in the border regions will be low, thus limiting temperature increase, and with that limiting the effect of an incorrect anatomy on temperature increase, in those regions. Electromagnetic-field calculations were performed, for each of the four antennas separately, using the finite difference time domain method with perfectly matched layer boundary conditions [42].
Feasibility of on-line temperature-based hyperthermia treatment planning to improve tumour temperatures during locoregional hyperthermia
Published in International Journal of Hyperthermia, 2018
H. P. Kok, L. Korshuize-van Straten, A. Bakker, R. de Kroon – Oldenhof, G. H. Westerveld, E. Versteijne, L. J. A. Stalpers, J. Crezee
Pre-treatment simulations to enable on-line use of hyperthermia treatment planning were performed using the Plan2Heat software package [32]. A hyperthermia planning CT scan was made in treatment position on a water bolus and mattresses. Hounsfield unit-based segmentation was performed to distinguish bone, muscle, fat and air [33] and the tumour was delineated by a radiation oncologist. Literature-based dielectric and thermal properties were assigned to the segmented tissues, as listed in Table 1 [34,35]. The models for the waveguides and water boluses were combined with the segmented patient model and electromagnetic fields were calculated for each individual antenna on a 2.5 × 2.5 × 2.5 mm3 grid using the “Finite Difference Time Domain” method [36,37]. For efficient temperature calculations the temperature at any location (x, y, z) in the patient resulting from a system with four active antennas, can be written as T00 is a constant resulting from the boundary conditions [38]. The feed vector v is a 4 × 1 vector containing the amplitudes and phases, vH is the complex conjugate transpose of v. The elements in the matrix 39]. Thus, superposition of these pre-computed temperature distributions as in Equation (1) allows an instant update of the predicted temperature distribution when the operator varies phase-amplitude settings during the on-line optimisation process [29,38]. The efficiency of the AMC-8 system is 70% [21], which was accounted for in the simulations.