Anatomical (MRI) and functional (SPECT) imaging provide different information and a combination of these modalities improves diagnostic specificity.

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.

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The introduction of functional data into the radiotherapy treatment planning process is currently the focus of significant commercial, technical, scientific and clinical development. The potential of such data from positron emission tomography (PET) was recognized at an early stage and was integrated into the radiotherapy treatment planning process through the use of image fusion software. The combination of PET and CT in a single system (PET/CT) to form an inherently fused anatomical and functional dataset has provided an imaging modality which could be used as the prime tool in the delineation of tumour volumes and the preparation of patient treatment plans, especially when integrated with virtual simulation. PET imaging typically using 18F-Fluorodeoxyglucose (18F-FDG) can provide data on metabolically active tumour volumes. These functional data have the potential to modify treatment volumes and to guide treatment delivery to cells with particular metabolic characteristics. This paper reviews the current status of the integration of PET and PET/CT data into the radiotherapy treatment process. Consideration is given to the requirements of PET/CT data acquisition with reference to patient positioning aids and the limitations imposed by the PET/CT system. It also reviews the approaches being taken to the definition of functional/tumour volumes and the mechanisms available to measure and include physiological motion into the imaging process. The use of PET data must be based upon a clear understanding of the interpretation and limitations of the functional signal. Protocols for the implementation of this development remain to be defined, and outcomes data based upon clinical trials are still awaited.