A Guide for Water Bolus Temperature Selection for Semi-Deep Head and Neck Hyperthermia Treatments Using the HYPERcollar3D Applicator.

During hyperthermia cancer treatments, especially in semi-deep hyperthermia in the head and neck (H&N) region, the induced temperature pattern is the result of a complex interplay between energy delivery and tissue cooling. The purpose of this study was to establish a water bolus temperature guide for the HYPERcollar3D H&N applicator. First, we measured the HYPERcollar3D water bolus heat-transfer coefficient. Then, for 20 H&N patients and phase/amplitude settings of 93 treatments we predict the T50 for nine heat-transfer coefficients and ten water bolus temperatures ranging from 20-42.5 °C. Total power was always tuned to obtain a maximum of 44 °C in healthy tissue in all simulations. As a sensitivity study we used constant and temperature-dependent tissue cooling properties. We measured a mean heat-transfer coefficient of h = 292 W m-2K-1 for the HYPERcollar3D water bolus. The predicted T50 shows that temperature coverage is more sensitive to the water bolus temperature than to the heat-transfer coefficient. We propose changing the water bolus temperature from 30 °C to 35 °C which leads to a predicted T50 increase of +0.17/+0.55 °C (constant/temperature-dependent) for targets with a median depth < 20 mm from the skin surface. For deeper targets, maintaining a water bolus temperature at 30 °C is proposed.

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| < 0.01 °C). In three cases, where the major vessels transverse target volume, notable drops (|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.

Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning.

A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck. Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available. The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used.Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

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 (ΔT) and the hyperthermia treatment quality (ΔT50) 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 (ΔT) improved significantly using patient-specific tissue properties, i.e. 1.0 °C (inter-quartile range (IQR) 0.8 °C) compared to 1.3 °C (IQR 0.7 °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, ΔT50 significantly improved, i.e. ΔT50 reduced from 0.9 °C (IQR 0.8 °C) to 0.4 °C (IQR 0.5 °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 °C.

MRI integration into treatment planning of head and neck tumors: Can patient immobilization be avoided?

To assess whether deformable registration between CT and MR images can be used to avoid patient immobilization, we compared registration accuracy in various scenarios, with and without immobilization equipment. Whereas both deformable registration and the use of immobilization equipment improved the registration accuracy, the combination gave the best alignment.

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 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.

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 (MRI-T2w) 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 (Tmax) 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 (Tmax: 38.0 °C) and CT and MRI (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 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.

Temperature simulations in hyperthermia treatment planning of the head and neck region: rigorous optimization of tissue properties.

BACKGROUND AND PURPOSE: 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. PATIENTS AND METHODS: 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 (∆T). The improvement in ∆T for optimized thermal property values was assessed by comparison with the ∆T for values from the literature, i.e., baseline and under thermal stress. RESULTS: The optimized perfusion and conductivity values of tumor, muscle, and fat led to an improvement in simulation accuracy (∆T: 2.1 ± 1.2 °C) compared with the accuracy for baseline (∆T: 12.7 ± 11.1 °C) or thermal stress (∆T: 4.4 ± 3.5 °C) property values. CONCLUSION: 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.

Feasibility of multimodal deformable registration for head and neck tumor treatment planning.

PURPOSE: To investigate the feasibility of using deformable registration in clinical practice to fuse MR and CT images of the head and neck for treatment planning. METHOD AND MATERIALS: A state-of-the-art deformable registration algorithm was optimized, evaluated, and compared with rigid registration. The evaluation was based on manually annotated anatomic landmarks and regions of interest in both modalities. We also developed a multiparametric registration approach, which simultaneously aligns T1- and T2-weighted MR sequences to CT. This was evaluated and compared with single-parametric approaches. RESULTS: Our results show that deformable registration yielded a better accuracy than rigid registration, without introducing unrealistic deformations. For deformable registration, an average landmark alignment of approximatively 1.7 mm was obtained. For all the regions of interest excluding the cerebellum and the parotids, deformable registration provided a median modified Hausdorff distance of approximatively 1 mm. Similar accuracies were obtained for the single-parameter and multiparameter approaches. CONCLUSIONS: This study demonstrates that deformable registration of head-and-neck CT and MR images is feasible, with overall a significanlty higher accuracy than for rigid registration.

Computational fluid dynamics in Fontan patients to evaluate power loss during simulated exercise.

OBJECTIVE: Exercise intolerance is common in total cavopulmonary connection (TCPC) patients. It has been suggested that power loss (Ploss) inside the TCPC plays a role in reduced exercise performance. Our objective is to establish the role of Ploss inside the TCPC during increased flow, simulating exercise in a patient-specific way. METHODS: Cardiac MRI (CMR) was used to obtain flow rates from the caval veins during rest and increased flow, simulating exercise with dobutamine. A 3D reconstruction of the TCPC was created using CMR data. Computational fluid dynamics (CFD) simulations were performed to calculate Ploss inside the TCPC structure for rest and stress conditions. To reflect the flow distribution during exercise, a condition where inferior caval vein (IVC) flow was increased twofold compared with rest was added. 29 TCPC patients (15 intra-atrial lateral tunnel (ILT) and 14 extracardiac conduit (ECC)) were included. RESULTS: Mean Ploss at rest was 1.36 ± 0.94 (ILT) and 3.20 ± 1.26 (ECC) mW/m(2) (p<0.001), 2.84 ±1.95 (ILT) and 8.41 ± 3.77 (ECC) mW/m(2) (p<0.001) during dobutamine and 5.21 ± 3.50 (ILT) and 15.28 ± 8.30 (ECC) mW/m(2) (p=0.001) with twofold IVC flow. The correlation between cardiac index and Ploss was exponential (ILT: R(2)=0.811, p<0.001; ECC: R(2)=0.690, p<0.001). CONCLUSIONS: Ploss inside the TCPC structure is limited but increases with simulated exercise. This relates to the anatomy of TCPC and the surgical technique used. In all flow conditions, ILT patients have lower Ploss than ECC patients. We did not find a relationship between Ploss and exercise capacity.

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.

Validation of MR thermometry: method for temperature probe sensor registration accuracy in head and neck phantoms.

PURPOSE: Magnetic resonance thermometry (MRT) is an attractive means to non-invasively monitor in vivo temperature during head and neck hyperthermia treatments because it can provide multi-dimensional temperature information with high spatial resolution over large regions of interest. However, validation of MRT measurements in a head and neck clinical set-up is crucial to ensure the temperature maps are accurate. Here we demonstrate a unique approach for temperature probe sensor localisation in head and neck hyperthermia test phantoms. METHODS: We characterise the proton resonance frequency shift temperature coefficient and validate MRT measurements in an oil-gel phantom by applying a combination of MR imaging and 3D spline fitting for accurate probe localisation. We also investigate how uncertainties in both the probe localisation and the proton resonance frequency shift (PRFS) thermal coefficient affect the registration of fibre-optic reference temperature probe and MRT readings. RESULTS: The method provides a two-fold advantage of sensor localisation and PRFS thermal coefficient calibration. We provide experimental data for two distinct head and neck phantoms showing the significance of this method as it mitigates temperature probe localisation errors and thereby increases accuracy of MRT validation results. CONCLUSIONS: The techniques presented here may be used to simplify calibration experiments that use an interstitial heating device, or any heating method that provides rapid and spatially localised heat distributions. Overall, the experimental verification of the data registration and PRFS thermal coefficient calibration technique provides a useful benchmarking method to maximise MRT accuracy in any similar context.

Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling.

PURPOSE: Hyperthermia treatment of head and neck tumors requires accurate treatment planning, based on 3D patient models that are derived from segmented 3D images. These segmentations are currently obtained by manual outlining of the relevant tissue regions, which is a tedious and time-consuming procedure (≈ 8 h) limiting the clinical applicability of hyperthermia treatment. In this context, the authors present and evaluate an automatic segmentation algorithm for CT images of the head and neck. METHODS: The proposed method combines anatomical information, based on atlas registration, with local intensity information in a graph cut framework. The method is evaluated with respect to ground truth manual delineation and compared with multiatlas-based segmentation on a dataset of 18 labeled CT images using the Dice similarity coefficient (DSC), the mean surface distance (MSD), and the Hausdorff surface distance (HSD) as evaluation measures. On a subset of 13 labeled images, the influence of different labelers on the method's accuracy is quantified and compared with the interobserver variability. RESULTS: For the DSC, the proposed method performs significantly better for the segmentation of all the tissues, except brain stem and spinal cord. The MSD shows a significant improvement for optical nerve, eye vitreous humor, lens, and thyroid. For the HSD, the proposed method performs significantly better for eye vitreous humor and brainstem. The proposed method has a significantly better score for DSC, MSD, and HSD than the multiatlas-based method for the eye vitreous humor. For the majority of the tissues (8/11) the segmentation accuracy of the proposed method is approaching the interobserver agreement. The authors' method showed better robustness to variations in atlas labeling compared with multiatlas segmentation. Moreover, the method improved the segmentation reproducibility compared with human observer's segmentations. CONCLUSIONS: In conclusion, the proposed framework provides in an accurate automatic segmentation of head and neck tissues in CT images for the generation of 3D patient models, which improves reproducibility, and substantially reduces labor involved in therapy planning.