Patient Modeling for Simulation Guided Head and Neck Hyperthermia

The prognosis for patients with advanced head and neck (H&N) cancer is very poor and treatment is challenging. These patients are standardly treated with radiotherapy with or without chemotherapy. These treatments are associated with a high treatment related late toxicity that affects the quality of life and social contacts in 20% of the patients. Side effects reported in all studies concern loss of swallowing and salivary function leading to difficulties in speaking and eating. Clinical studies have shown that the addition of a local hyperthermia treatment to a (chemo)radiation treatment signifcantly improve the treatment outcome without increasing treatment related late toxicity. Application of hyperthermia in the challenging H&N region using multi antenna-element applicators requires hyperthermia treatment planning (HTP). This thesis focuses on the development of an accurate and efficient patient model generation for patient-specific HTP based on electromagnetic (EM) and temperature modeling. The first two chapters include the automatic generation of 3D patient-models from Computed Tomography (CT) scans (chapter 2) or CT and Magnetic Resonance Imaging (MRI) scans (chapter 3). Chapter 4 and 5 include temperature simulation guided H&N hyperthermia using optimized thermal tissue property values

Feasibility and relevance of discrete vasculature modeling in routine hyperthermia treatment planning

Purpose: To investigate the effect of patient specific vessel cooling on head and neck hyperthermia treatment planning (HTP). Methods and materials: Twelve patients undergoing radiotherapy were scanned using computed tomography (CT), magnetic resonance imaging (MRI) and contrast enhanced MR angiography (CEMRA). 3D patient models were constructed using the CT and MRI data. The arterial vessel tree was constructed from the MRA images using the ‘graph-cut’ method, combining information from Frangi vesselness filtering and region growing, and the results were validated against manually placed markers in/outside the vessels. Patient specific HTP was performed and the change in thermal distribution prediction caused by arterial cooling was evaluated by adding discrete vasculature (DIVA) modeling to the Pennes bioheat equation (PBHE). Results: Inclusion of arterial cooling showed a relevant impact, i.e., DIVA modeling predicts a decreased treatment quality by on average 0.19 °C (T90), 0.32 °C (T50) and 0.35 °C (T20) that is robust against variations in the inflow blood rate (|ΔT| 0.5 °C) were observed. Conclusion: Addition of patient-specific DIVA into the thermal modeling can significantly change predicted treatment quality. In cases where clinically detectable vessels pass the heated region, we advise to perform DIVA modeling

Impact of Head Morphology on Local Brain Specific Absorption Rate From Exposure to Mobile Phone Radiation

Among various possible health effects of mobile phone radiation, the risk of inducing cancer has the strongest interest of laymen and health organizations. Recently, the Interphone epidemiological study investigated the association between the estimated Radio Frequency (RF) dose from mobile phones and the risk of developing a brain tumor. Their dosimetric analysis included over 100 phone models but only two homogeneous head phantoms. So, the potential impact of individual morphological features on global and local RF absorption in the brain was not investigated. In this study, we performed detailed dosimetric simulations for 20 head models and quantified the variation of RF dose in different brain regions as a function of head morphology. Head models were exposed to RF fields from generic mobile phones at 835 and 1900MHz in the tilted and cheek positions. To evaluate the local RF dose variation, we used and compared two different post-processing methods, that is, averaging specific absorption rate (SAR) over Talairach regions and over sixteen predefined 1cm(3) cube-shaped field-sensors. The results show that the variation in the averaged SAR among the heads can reach up to 16.4dB at a 1cm(3) cube inside the brain (field-sensor method) and alternatively up to 15.8dB in the medulla region (Talairach method). In conclusion, we show head morphology as an important uncertainty source for dosimetric studies of mobile phones. Therefore, any dosimetric analysis dealing with RF dose at a specific region in the brain (e.g., tumor risk analysis) should be based upon real morphology. Bioelectromagnetics. 35:66-76, 2015. (c) 2014 Wiley Periodicals, Inc

Impact of head morphology on local brain specific absorption rate from exposure to mobile phone radiation

\u3cp\u3eAmong various possible health effects of mobile phone radiation, the risk of inducing cancer has the strongest interest of laymen and health organizations. Recently, the Interphone epidemiological study investigated the association between the estimated Radio Frequency (RF) dose from mobile phones and the risk of developing a brain tumor. Their dosimetric analysis included over 100 phone models but only two homogeneous head phantoms. So, the potential impact of individual morphological features on global and local RF absorption in the brain was not investigated. In this study, we performed detailed dosimetric simulations for 20 head models and quantified the variation of RF dose in different brain regions as a function of head morphology. Head models were exposed to RF fields from generic mobile phones at 835 and 1900 MHz in the tilted and cheek positions. To evaluate the local RF dose variation, we used and compared two different post-processing methods, that is, averaging specific absorption rate (SAR) over Talairach regions and over sixteen predefined 1 cm(3) cube-shaped field-sensors. The results show that the variation in the averaged SAR among the heads can reach up to 16.4 dB at a 1 cm(3) cube inside the brain (field-sensor method) and alternatively up to 15.8 dB in the medulla region (Talairach method). In conclusion, we show head morphology as an important uncertainty source for dosimetric studies of mobile phones. Therefore, any dosimetric analysis dealing with RF dose at a specific region in the brain (e.g., tumor risk analysis) should be based upon real morphology.\u3c/p\u3

Temperature simulations in hyperthermia treatment planning of the head and neck region Rigorous optimization of tissue properties

Hyperthermia treatment planning (HTP) is used in the head and neck region (H&N) for pretreatment optimization, decision making, and real-time HTP-guided adaptive application of hyperthermia. In current clinical practice, HTP is based on power-absorption predictions, but thermal dose-effect relationships advocate its extension to temperature predictions. Exploitation of temperature simulations requires region- and temperature-specific thermal tissue properties due to the strong thermoregulatory response of H&N tissues. The purpose of our work was to develop a technique for patient group-specific optimization of thermal tissue properties based on invasively measured temperatures, and to evaluate the accuracy achievable. Data from 17 treated patients were used to optimize the perfusion and thermal conductivity values for the Pennes bioheat equation-based thermal model. A leave-one-out approach was applied to accurately assess the difference between measured and simulated temperature (a dagger T). The improvement in a dagger T for optimized thermal property values was assessed by comparison with the a dagger T for values from the literature, i.e., baseline and under thermal stress. The optimized perfusion and conductivity values of tumor, muscle, and fat led to an improvement in simulation accuracy (a dagger T: 2.1 +/- 1.2 A degrees C) compared with the accuracy for baseline (a dagger T: 12.7 A +/- 11.1 A degrees C) or thermal stress (a dagger T: 4.4 A +/- 3.5 A degrees C) property values. The presented technique leads to patient group-specific temperature property values that effectively improve simulation accuracy for the challenging H&N region, thereby making simulations an elegant addition to invasive measurements. The rigorous leave-one-out assessment indicates that improvements in accuracy are required to rely only on temperature-based HTP in the clinic

Accurate 3D temperature dosimetry during hyperthermia therapy by combining invasive measurements and patient-specific simulations

Purpose: Dosimetry during deep local hyperthermia treatments in the head and neck currently relies on a limited number of invasively placed temperature sensors. The purpose of this study was to assess the feasibility of 3D dosimetry based on patient-specific temperature simulations and sensory feedback. Materials and methods: The study includes 10 patients with invasive thermometry applied in at least two treatments. Based on their invasive thermometry, we optimised patient-group thermal conductivity and perfusion values for muscle, fat and tumour using a 'leave-one-out' approach. Next, we compared the accuracy of the predicted temperature (Delta T) and the hyperthermia treatment quality (Delta T50) of the optimisations based on the patient-group properties to those based on patient-specific properties, which were optimised using previous treatment measurements. As a robustness check, and to enable comparisons with previous studies, we optimised the parameters not only for an applicator efficiency factor of 40%, but also for 100% efficiency. Results: The accuracy of the predicted temperature (Delta T) improved significantly using patient-specific tissue properties, i.e. 1.0 degrees C (inter-quartile range (IQR) 0.8 degrees C) compared to 1.3 degrees C (IQR 0.7 degrees C) for patient-group averaged tissue properties for 100% applicator efficiency. A similar accuracy was found for optimisations using an applicator efficiency factor of 40%, indicating the robustness of the optimisation method. Moreover, in eight patients with repeated measurements in the target region, Delta T50 significantly improved, i.e. Delta T50 reduced from 0.9 degrees C (IQR 0.8 degrees C) to 0.4 degrees C (IQR 0.5 degrees C) using an applicator efficiency factor of 40%. Conclusion: This study shows that patient-specific temperature simulations combined with tissue property reconstruction from sensory data provides accurate minimally invasive 3D dosimetry during hyperthermia treatments: T50 in sessions without invasive measurements can be predicted with a median accuracy of 0.4 degrees C

CT-based patient modeling for head and neck hyperthermia treatment planning:manual versus automatic normal-tissue-segmentation

\u3cp\u3eBACKGROUND AND PURPOSE: Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality.\u3c/p\u3e\u3cp\u3eMATERIAL AND METHODS: CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties.\u3c/p\u3e\u3cp\u3eRESULTS: Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%.\u3c/p\u3e\u3cp\u3eCONCLUSIONS: Automatically generated 3D patient models can be introduced in the clinic for H&N HTP.\u3c/p\u3

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

Purpose: In current clinical practice, head and neck (H& N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H& N HTP. Methods: CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRIT2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H& N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H& N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (T-max) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb. Results: In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (T-max: 38.0. degrees C) and CT and MRI (T-max: 38.1. degrees C) result in similar simulated temperatures, while CT and MRIdb (T-max: 38.5. C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Conclusions: Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia. (C) 2014 American of Physicists in Medicine

CT-based patient modeling for head and neck hyperthermia treatment planning: manual versus automatic normal-tissue-segmentation

BACKGROUND AND PURPOSE: Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. MATERIAL AND METHODS: CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. RESULTS: Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. CONCLUSIONS: Automatically generated 3D patient models can be introduced in the clinic for H&N HTP