In this paper, the authors' review the applicability of the open-source GATE Monte Carlo simulation platform based on the GEANT4 toolkit for radiation therapy and dosimetry applications. The many applications of GATE for state-of-the-art radiotherapy simulations are described including external beam radiotherapy, brachytherapy, intraoperative radiotherapy, hadrontherapy, molecular radiotherapy, and in vivo dose monitoring. Investigations that have been performed using GEANT4 only are also mentioned to illustrate the potential of GATE. The very practical feature of GATE making it easy to model both a treatment and an imaging acquisition within the same framework is emphasized. The computational times associated with several applications are provided to illustrate the practical feasibility of the simulations using current computing facilities.

The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.

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Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.

PET allows the imaging of functional properties of the living tissue, whereas other modalities (CT, MRI) provide structural information at significantly higher resolution and better image quality. Constraints for injected radioactivity, technologic limitations of current instrumentation, and inherent spatial uncertainties on the decaying process affect the quality of PET images. In this article we illustrate how structural information of matched anatomic images can be used in a multiresolution model to enhance the signal-to-noise ratio of PET images. The model states a flexible relation between function and structure in the brain and replaces high-resolution information of PET images with appropriately scaled MRI or CT local detail. The method can be naturally extended to other functional imaging modalities (SPECT, functional MRI). Methods: The methodology is based on the multiresolution property of the wavelet transform (WT). First, the coregistered structural image (MRI/CT) is downgraded to the resolution of the PET volume through appropriate filtering. Second, a redundant version of the WT is applied to both volumes. Third, a linear model is applied to the set of local coefficients of both image volumes and resulting parameters are recorded. The overall set of linear coefficients is then modeled as a mixture of multivariate gaussian distributions and fitted through a k-means algorithm. Finally, the local wavelet coefficients of the PET image are substituted by the corresponding values of the MRI/CT set calibrated according to the resulting clustering. The methodology was validated on digital simulated images and clinical data to evaluate its quantitative potential for individual as well as group analysis. Results: Application to real and simulated datasets shows very effective noise reduction (15% SD) while resolution is preserved. Conclusion: The methodology is robust to errors in the coregistration parameters, practical to implement, and computationally fast.

The proposed stereo-ToF system allows for sufficient accuracy and faster patient repositioning in radiotherapy. Its capability to track the complete patient surface in real time could allow, in the future, not only for an accurate positioning but also a real time tracking of any patient intrafraction motion (translation, involuntary, and breathing).

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