Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner. A list-mode-based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction forC-Pittsburgh compound B (C-PIB) scans using simulated and actual data with well-controlled motion. An efficient list-mode-based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to theC-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini-Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%. The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.
Our results suggest that the AGE content of a protein load is responsible for renal hemodynamic modifications. Therefore, prevention of diabetic nephropathy progression could aim predominantly at reducing food AGE content.
IntroductionSerotonin is involved in a variety of physiological functions and brain disorders. In this context, efforts have been made to investigate the in vivo fluctuations of this neurotransmitter using positron emission tomography (PET) imaging paradigms. Since serotonin is a full agonist, it binds preferentially to G-protein coupled receptors. In contrast, antagonist PET ligands additionally interact with uncoupled receptors. This could explain the lack of sensitivity to serotonin fluctuations of current 5-HT1A radiopharmaceuticals which are mainly antagonists and suggests that agonist radiotracers would be more appropriate to measure changes in neurotransmitter release. The present study evaluated the sensitivity to endogenous serotonin release of a recently developed, selective 5-HT1A receptor PET radiopharmaceutical, the agonist [18F]F13640 (a.k.a. befiradol or NLX-112).Materials and MethodsFour cats each underwent three PET scans with [18F]F13640, i.e., a control PET scan of 90 min, a PET scan preceded 30 min before by an intravenous injection 1 mg/kg of d-fenfluramine, a serotonin releaser (blocking challenge), and a PET scan comprising the intravenous injection of 1 mg/kg of d-fenfluramine 30 min after the radiotracer injection (displacement challenge). Data were analyzed with regions of interest and voxel-based approaches. A lp-ntPET model approach was implemented to determine the dynamic of serotonin release during the challenge study.ResultsD-fenfluramine pretreatment elicited a massive inhibition of [18F]F13640 labeling in regions known to express 5-HT1A receptors, e.g., raphe nuclei, hippocampus, thalamus, anterior cingulate cortex, caudate putamen, occipital, frontal and parietal cortices, and gray matter of cerebellum. Administration of d-fenfluramine during PET acquisition indicates changes in occupancy from 10% (thalamus) to 31% (gray matter of cerebellum) even though the dissociation rate of [18F]F13640 over the 90 min acquisition time was modest. The lp-ntPET simulation succeeded in differentiating the control and challenge conditions.ConclusionThe present findings demonstrate that labeling of 5-HT1A receptors with [18F]F13640 is sensitive to serotonin concentration fluctuations in vivo. Although the data underline the need to perform longer PET scan to ensure accurate measure of displacement, they support clinical development of [18F]F13640 as a tool to explore experimental paradigms involving physiological or pathological (neurological or neuropsychiatric pathologies) fluctuations of extracellular serotonin.
This paper presents a Bayesian algorithm for PET image segmentation. The proposed method, which is derived from PET physics, models tissue activity using a mixture of PoissonGamma distributions. Moreover, a Markov field is proposed to model the spatial correlation between mixture components. Then, segmentation is performed using an Markov chain Monte Carlo algorithm that jointly estimates the mixture parameters and classifies voxels. The performance of the proposed algorithm is illustrated on synthetic and real data. Experimental results on real chest PET images suggest that the proposed method can correctly segment both small and large tumors.
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