We describe a fully-automatic 3D-segmentation technique for brain MR images. By

Until recently, no direct comparison between [15O]water positron emission tomography (PET) and arterial spin labeling (ASL) for measuring cerebral blood flow (CBF) was possible. With the introduction of integrated, hybrid magnetic resonance (MR)-PET scanners, such a comparison becomes feasible. This study presents results of CBF measurements recorded simultaneously with [15O]water and ASL. A 3T MR-BrainPET scanner was used for the simultaneous acquisition of pseudo-continuous ASL (pCASL) magnetic resonance imaging (MRI) and [15O]water PET. Quantitative CBF values were compared in 10 young healthy male volunteers at baseline conditions. A statistically significant (P<0.05) correlation was observed between the two modalities; the whole-brain CBF values determined with PET and pCASL were 43.3±6.1 mL and 51.9±7.1 mL per 100 g per minute, respectively. The gray/white matter (GM/WM) ratio of CBF was 3.0 for PET and 3.4 for pCASL. A paired t-test revealed differences in regional CBF between ASL and PET with higher ASL-CBF than PET-CBF values in cortical areas. Using an integrated, hybrid MR-PET a direct simultaneous comparison between ASL and [15O]water PET became possible for the first time so that temporal, physiologic, and functional variations were avoided. Regional and individual differences were found despite the overall similarity between ASL and PET, requiring further detailed investigations.

Using positron emission tomography (PET), measurements of the regional cerebral metabolic rate of glucose (rCMRGlc) are able to delineate cerebral metabolic responses to external or mental stimulation. In order to examine possible changes of brain metabolism due to Yoga meditation PET scans were performed in 8 members of a Yoga meditation group during the normal control state (C) and Yoga meditative relaxation (YMR). Whereas there were intraindividual changes of the total CMRGlc, the alterations were not significant for intergroup comparison; specific focal changes or changes in the interhemispheric differences in metabolism were also not seen; however the ratios of frontal vs. occipital rCMRGlc were significantly elevated (p < 0.05) during YMR. These altered ratios were caused by a slight increase of frontal rCMRGlc and a more pronounced reduction in primary and secondary visual centers. These data indicate a holostic behavior of the brain metabolism during the time of altered state of consciousness during YMR.

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Initial experiences with the hybrid MRI/BrainPET indicate a promising basis for further developments of this unique technique allowing simultaneous PET imaging combined with both anatomical and functional MRI.

Both F-FET PET and PWI discriminate LGG from HGG with similar diagnostic performance. Regional abnormalities in the tumor area are usually not congruent indicating that tumor grading byF-FET PET and PWI is based on different pathophysiological phenomena.

Using statistical methods the reconstruction of positron emission tomography (PET) images can be improved by high-resolution anatomical information obtained from magnetic resonance (MR) images. We implemented two approaches that utilize MR data for PET reconstruction. The anatomical MR information is modeled as a priori distribution of the PET image and combined with the distribution of the measured PET data to generate the a posteriori function from which the expectation maximization (EM)-type algorithm with a maximum a posteriori (MAP) estimator is derived. One algorithm (Markov-GEM) uses a Gibbs function to model interactions between neighboring pixels within the anatomical regions. The other (Gauss-EM) applies a Gauss function with the same mean for all pixels in a given anatomical region. A basic assumption of these methods is that the radioactivity is homogeneously distributed inside anatomical regions. Simulated and phantom data are investigated under the following aspects: count density, object size, missing anatomical information, and misregistration of the anatomical information. Compared with the maximum likelihood-expectation maximization (ML-EM) algorithm the results of both algorithms show a large reduction of noise with a better delineation of borders. Of the two algorithms tested, the Gauss-EM method is superior in noise reduction (up to 50%). Regarding incorrect a priori information the Gauss-EM algorithm is very sensitive, whereas the Markov-GEM algorithm proved to be stable with a small change of recovery coefficients between 0.5 and 3%.

• Dual-time-point imaging is equivalent to dynamic FET PET for grading of gliomas. • Dual-time-point imaging is less time consuming than dynamic FET PET. • Costs can be reduced due to higher patient throughput. • Reduced imaging time increases patient comfort and sedation might be avoided. • Quicker image interpretation is possible, as no curve evaluation is necessary.