Regional deep hyperthermia: quantitative evaluation of predicted and direct measured temperature distributions in patients with high-risk extremity soft-tissue sarcoma.

BACKGROUND: Temperature distributions resulting from hyperthermia treatment of patients with high-risk soft-tissue sarcoma (STS) were quantitatively evaluated and globally compared with thermal simulations performed by a treatment planning system. The aim was to test whether the treatment planning system was able to predict correct temperature distributions. METHODS: Five patients underwent computed tomography (CT) fluoroscopy-guided placement of tumor catheters used for the interstitial temperature measurements. For the simulations, five 3 D patient models were reconstructed by segmenting the patient CT datasets into different tissues. The measured and simulated data were evaluated by calculating the temperature change ( ΔT ), T90, T50, T20, Tmean, Tmin and Tmax, as well as the 90th percentile thermal dose (CEM43T90). In order to measure the agreement between both methods quantitatively, the Bland-Altman analysis was applied. RESULTS: The absolute difference between measured and simulated temperatures were found to be 2°, 6°, 1°, 4°, 5° and 4 °C on average for Tmin, Tmax, T90, T50, T20 and Tmean, respectively. Furthermore, the thermal simulations exhibited relatively higher thermal dose compared to those that were measured. Finally, the results of the Bland-Altman analysis showed that the mean difference between both methods was above 2 °C which is considered to be clinically unacceptable. CONCLUSION: Given the current practical limitations on resolution of calculation grid, tissue properties, and perfusion information, the software SigmaHyperPlan™ is incapable to produce thermal simulations with sufficient correlation to typically heterogeneous tissue temperatures to be useful for clinical treatment planning.

Inducing intravesical hyperthermia of the ex-vivo porcine bladder wall: radiofrequency-induction versus recirculation using a custom-made device.

INTRODUCTION: Several techniques can be used to treat intravesical chemohyperthermia (ChHT). We compared radiofrequency-induced hyperthermia (RF-HT) with conductive hyperthermia (C-HT) for their ability to induce bladder wall temperatures of >40.5 °C, the target temperature for ChHT. MATERIALS AND METHODS: Fresh porcine bladders (n = 12) were placed in a temperature-controlled saline bath to simulate body temperature and circulation. HT was induced with RF-HT (43 °C) or C-HT (inflow temperature 44 and 46 °C) using a custom-made device. In two additional bladders, we varied intravesical solution and volume. Temperatures were recorded with a three-way catheter containing three mucosal and two urethral thermocouples (TCs) and a 915 MHz RF antenna, and with external TCs in the bladder wall at three different levels and three different locations. RESULTS: Target temperature (40.5 °C) was reached in the submucosa at all locations by both techniques. In the detrusor, target temperature was reached by RF-HT at the bladder neck and side wall. C-HT46 reached significantly higher submucosal temperatures at the side wall. The bladder dome seemed best heated by C-HT, although a high inflow temperature (46 vs. 44 °C) was required (ns). Intravesical saline resulted in higher temperatures than sterile water for RF-HT. A volume of 100 mL resulted in higher bladder dome temperatures for RF-HT, and higher bladder neck with lower dome temperatures for C-HT. CONCLUSION: Our results indicate a slightly superior heating capacity for RF-HT compared to C-HT, whereas for the bladder dome, the reverse seems true. Comparative studies are warranted to evaluate whether HT efficacy differs between both techniques, with emphasis on tumor location.

GATE Monte Carlo simulations for variations of an integrated PET/MR hybrid imaging system based on the Biograph mMR model.

A simulation toolkit, GATE (Geant4 Application for Tomographic Emission), was used to develop an accurate Monte Carlo (MC) simulation of a fully integrated 3T PET/MR hybrid imaging system (Siemens Biograph mMR). The PET/MR components of the Biograph mMR were simulated in order to allow a detailed study of variations of the system design on the PET performance, which are not easy to access and measure on a real PET/MR system. The 3T static magnetic field of the MR system was taken into account in all Monte Carlo simulations. The validation of the MC model was carried out against actual measurements performed on the PET/MR system by following the NEMA (National Electrical Manufacturers Association) NU 2-2007 standard. The comparison of simulated and experimental performance measurements included spatial resolution, sensitivity, scatter fraction, and count rate capability. The validated system model was then used for two different applications. The first application focused on investigating the effect of an extension of the PET field-of-view on the PET performance of the PET/MR system. The second application deals with simulating a modified system timing resolution and coincidence time window of the PET detector electronics in order to simulate time-of-flight (TOF) PET detection. A dedicated phantom was modeled to investigate the impact of TOF on overall PET image quality. Simulation results showed that the overall divergence between simulated and measured data was found to be less than 10%. Varying the detector geometry showed that the system sensitivity and noise equivalent count rate of the PET/MR system increased progressively with an increasing number of axial detector block rings, as to be expected. TOF-based PET reconstructions of the modeled phantom showed an improvement in signal-to-noise ratio and image contrast to the conventional non-TOF PET reconstructions. In conclusion, the validated MC simulation model of an integrated PET/MR system with an overall accuracy error of less than 10% can now be used for further MC simulation applications such as development of hardware components as well as for testing of new PET/MR software algorithms, such as assessment of point-spread function-based reconstruction algorithms.