Valerio Fortunati

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.

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.

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

Abstract 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.

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

PURPOSE An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. METHODS A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T2-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. RESULTS A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. CONCLUSIONS This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

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.

Hyperthermia critical tissues automatic segmentation of head and neck CT images using atlas registration and graph cuts

Outcome optimization of hyperthermia tumor treatment in the head and neck requires accurate hyperthermia treatment planning. Hyperthermia treatment planning is based on tissue segmentation for 3D patient model generation. We present here an automatic atlas-based segmentation algorithm for the organs at risk from CT images of the head and neck. To overcome the large anatomical variability, atlas registration and intensity-based classification were combined. A cost function composed of an intensity energy term, a spatial prior energy term based on the atlas registration and a regularization term is globally minimized using graph cut. The method was evaluated by measuring Dice similarity coefficient, mean and Hausdorff surface distances with respect to manual delineation. Overall a high correspondence was found with Dice similarity coefficient higher than 0.86 and a mean distance lower than the voxel resolution.

Association of acute adverse effects with high local SAR induced in the brain from prolonged RF head and neck hyperthermia

To provide an adequate level of protection for humans from exposure to radio-frequency (RF) electromagnetic fields (EMF) and to assure that any adverse health effects are avoided. The basic restrictions in terms of the specific energy absorption rate (SAR) were prescribed by IEEE and ICNIRP. An example of a therapeutic application of non-ionizing EMF is hyperthermia (HT), in which intense RF energy is focused at a target region. Deep HT in the head and neck (H&N) region involves inducing energy at 434 MHz for 60 min on target. Still, stray exposure of the brain is considerable, but to date only very limited side-effects were observed. The objective of this study is to investigate the stringency of the current basic restrictions by relating the induced EM dose in the brain of patients treated with deep head and neck (H&N) HT to the scored acute health effects. We performed a simulation study to calculate the induced peak 10 g spatial-averaged SAR (psSAR₁₀g) in the brains of 16 selected H&N patients who received the highest SAR exposure in the brain, i.e. who had the minimum brain-target distance and received high forwarded power during treatment. The results show that the maximum induced SAR in the brain of the patients can exceed the current basic restrictions (IEEE and ICNIRP) on psSAR₁₀g for occupational environments by 14 times. Even considering the high local SAR in the brain, evaluation of acute effects by the common toxicity criteria (CTC) scores revealed no indication of a serious acute neurological effect. In addition, this study provides pioneering quantitative human data on the association between maximum brain SAR level and acute adverse effects when brains are exposed to prolonged RF EMF.

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.

Total Correlation-Based Groupwise Image Registration for Quantitative MRI

In quantitative magnetic resonance imaging (qMRI), quantitative tissue properties can be estimated by fitting a signal model to the voxel intensities of a series of images acquired with different settings. To obtain reliable quantitative measures, it is necessary that the qMRI images are spatially aligned so that a given voxel corresponds in all images to the same anatomical location. The objective of the present study is to describe and evaluate a novel automatic groupwise registration technique using a dissimilarity metric based on an approximated form of total correlation. The proposed registration method is applied to five qMRI datasets of various anatomical locations, and the obtained registration performances are compared to these of a conventional pairwise registration based on mutual information. The results show that groupwise total correlation yields better registration performances than pairwise mutual information. This study also establishes that the formulation of approximated total correlation is quite analogous to two other groupwise metrics based on principal component analysis (PCA). Registration performances of total correlation and these two PCA-based techniques are therefore compared. The results show that total correlation yields performances that are analogous to these of the PCAbased techniques. However, compared to these PCA-based metrics, total correlation has two main advantages. Firstly, it is directly derived from a multivariate form of mutual information, while the PCA-based metrics were obtained empirically. Secondly, total correlation has the advantage of requiring no user-defined parameter.

Groupwise image registration of multimodal head-and-neck images

Fusion of multimodal medical images using deformable registration is of high interest for head-and-neck tumour treatment planning. In this context, more than two images often have to be aligned for a given patient. The conventional, pairwise way to register multiple images is to select one of them as fixed reference and independently align each remaining image with it. An alternative method would be to simultaneously register the images using a groupwise registration scheme, thus eliminating the need to select a reference image and avoiding any bias due to this arbitrary choice. In this study, we propose a novel groupwise image registration technique, combining a principal component analysis (PCA) based similarity metric and modality independent neighbourhood descriptors (MIND). Results on 16 patients show that the images are slightly better aligned when using the proposed registration method than when using pairwise registration.