Simulation techniques in hyperthermia treatment planning

Abstract

Abstract Clinical trials have shown that hyperthermia (HT), i.e. an increase of tissue temperature to 39-44 °C, significantly enhance radiotherapy and chemotherapy effectiveness [1]. Driven by the developments in computational techniques and computing power, personalised hyperthermia treatment planning (HTP) has matured and has become a powerful tool for optimising treatment quality. Electromagnetic, ultrasound, and thermal simulations using realistic clinical set-ups are now being performed to achieve patient-specific treatment optimisation. In addition, extensive studies aimed to properly implement novel HT tools and techniques, and to assess the quality of HT, are becoming more common. In this paper, we review the simulation tools and techniques developed for clinical hyperthermia, and evaluate their current status on the path from 'model' to 'clinic'. In addition, we illustrate the major techniques employed for validation and optimisation. HTP has become an essential tool for improvement, control, and assessment of HT treatment quality. As such, it plays a pivotal role in the quest to establish HT as an efficacious addition to multi-modality treatment of cancer.

Figure 1

Schematic workflow for EM-HTP, using head and neck hyperthermia as example. In US-HTP the EM simulation & optimization step is replaced by US simulation & optimization.

Figure 2

US pressure field prediction for a model of the InSightec ExAblate4000 system, illustrating the feasibility of 3D full-wave simulation for this system with 1024 independently driven transducers. The obtained pressure distribution is displayed with a logarithmic color scale on three orthogonal planes through the target. The distorting impact of tissue parameter inhomogeneity on the focus shape is clearly visible. Modeling allows to plan dynamic focus scanning, for contiguous heating of large tissue volumes.

Figure 3

Cross-sections of the PD for 400W (upper row) and temperature distributions (lower row) predicted by Sigma-HyperPlan for a large (left), average (middle) and slim (right) patient with a cervical tumor (red contour). Visible are also the slings on which the patient is positioned during deep HT (black or white circles on each side of the patient). The temperature distributions are obtained by increasing power until 44°C in healthy tissue is reached.

Figure 4

Combined use of segmentation, EM, and thermal solvers to generate initial temperature distributions in the pretreatment phase. During treatment, the MR temperature imaging (MRTI) provides feedback to empirically determine patient specific parameters, e.g., perfusion. This information was used to steer the beam effectively to the target tumor on right side of leg and away from muscle at the left of bone (67, 155).