PET image segmentation using a Gaussian mixture model and Markov random fields.

BACKGROUND: Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on many parameters. METHODS: An improved statistical approach using a Gaussian mixture model (GMM) is proposed to obtain initial estimates of a target volume, followed by a correction step based on a Markov random field (MRF) and a Gibbs distribution to account for dependencies among neighboring voxels. In order to evaluate the proposed algorithm, phantom measurements of spherical and non-spherical objects with the smallest diameter being 8 mm were performed at signal-to-background ratios (SBRs) between 2.06 and 9.39. Additionally (68)Ga-PET data from patients with lesions in the liver and lymph nodes were evaluated. RESULTS: The proposed algorithm produces stable results for different reconstruction algorithms and different lesion shapes. Furthermore, it outperforms all threshold methods regarding detection rate, determines the spheres' volumes more accurately than fixed threshold methods, and produces similar values as iterative thresholding. In a comparison with other statistical approaches, the algorithm performs equally well for larger volumes and even shows improvements for small volumes and SBRs. The comparison with experts' manual delineations on the clinical data shows the same qualitative behavior as for the phantom measurements. CONCLUSIONS: In conclusion, a generic probabilistic approach that does not require data measured beforehand is presented whose performance, robustness, and swiftness make it a feasible choice for PET segmentation.

Measurements of occupational and patient exposure as well as image quality for two C-arms.

Two different C-arms, a Philips Veradius and a Ziehm Vision FD were evaluated with regard to occupational and patient radiation exposure as well as image quality. For this, the scatter radiation, the entrance surface dose rate (ESD rate) and the low contrast detectability (LCD) were evaluated with regard to different examination modes, using phantoms with different thicknesses as well as a Leeds Test Object. The results show a large range of variance between the two systems in relation to the topics investigated. Within comparable modes the Phillips Veradius causes a lower occupational and patient exposure than the Ziehm Vision FD. Furthermore, a lower radiation burden does not necessarily decrease the image quality, and a slight increase in LCD for particular settings is foiled by a big escalation in patient dose.