Rainer Hinz

Amyloid, hypometabolism, and cognition in Alzheimer disease: An [11C]PIB and [18F]FDG PET study

Objective: To investigate the association between brain amyloid load in Alzheimer disease (AD) measured by [11C]PIB-PET, regional cerebral glucose metabolism (rCMRGlc) measured by [18F]FDG-PET, and cognition. Methods: Nineteen subjects with AD and 14 controls had [11C]PIB-PET and underwent a battery of psychometric tests. Twelve of those subjects with AD and eight controls had [18F]FDG-PET. Parametric images of [11C]PIB binding and rCMRGlc were interrogated with a region-of-interest atlas and statistical parametric mapping. [11C]PIB binding and rCMRGlc were correlated with scores on psychometric tests. Results: AD subjects showed twofold increases in mean [11C]PIB binding in cingulate, frontal, temporal, parietal, and occipital cortical areas. Higher cortical amyloid load correlated with lower scores on facial and word recognition tests. Two patients fulfilling the clinical criteria for AD had normal [11C]PIB at baseline. Over 20 months this remained normal in one but increased in the cingulate of the other. Mean levels of temporal and parietal rCMRGlc were reduced by 20% in AD and these correlated with mini mental scores, immediate recall, and recognition memory test for words. Higher [11C]PIB uptake correlated with lower rCMRGlc in temporal and parietal cortices. Conclusion: [11C]PIB-PET detected an increased amyloid plaque load in 89% of patients with clinically probable Alzheimer disease (AD). The high frontal amyloid load detected by [11C]PIB-PET in AD in the face of spared glucose metabolism is of interest and suggests that amyloid plaque formation may not be directly responsible for neuronal dysfunction in this disorder.

Brain inflammation is induced by co-morbidities and risk factors for stroke

Highlights ► Risk factors for stroke include atherosclerosis, obesity, diabetes and hypertension. ► Stroke risk factors are associated with peripheral inflammation. ► Corpulent rats and atherogenic mice show increased inflammation in the brain. ► Pilot data show that patients at risk of stroke may also develop brain inflammation. ► Chronic peripheral inflammation can drive inflammatory changes in the brain.

On the undecidability among kinetic models: from model selection to model averaging.

This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameters estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of “good” models reduces the “generalization error,” the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual models likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaikes information–theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [11C]flumazenil kinetics in the brain, containing both real and simulated data.

Positron emission tomography for quality assurance of cancer therapy with light ion beams

Positron emission tomography (PET) offers the possibility of in-situ monitoring the tumour treatment with light ion beams by means of imaging the spatial distribution of β − -activity that is produced as a byproduct of the therapeutic irradiation via nuclear fragmentation reactions between the projectiles and the atomic nuclei of the tissue within the target volume. The implementation of this PET technique at the experimental tumour therapy facility at the Gesellschaft fur Schwerionenforschung (GSI) in Darmstadt and first results of its clinical application are presented.

Microglia, amyloid, and glucose metabolism in Parkinson's disease with and without dementia.

[11C](R)PK11195-PET measures upregulation of translocator protein, which is associated with microglial activation, [11C]PIB-PET is a marker of amyloid, while [18F]FDG-PET measures cerebral glucose metabolism (rCMRGlc). We hypothesize that microglial activation is an early event in the Parkinson’s disease (PD) spectrum and is independent of the amyloid pathology. The aim of this study is to evaluate in vivo the relationship between microglial activation, amyloid deposition, and glucose metabolism in Parkinson’s disease dementia (PDD) and PD subjects without dementia. Here, we evaluated 11 PDD subjects, 8 PD subjects without dementia, and 24 control subjects. Subjects underwent T1 and T2 MRI, [11C](R)PK11195, [18F]FDG, and [11C]PIB PET scans. Parametric maps of [11C](R)PK11195 binding potential, rCMRGlc, and [11C]PIB uptake were interrogated using region of interest and SPM (statistical parametric mapping) analysis. The PDD patients showed a significant increase of microglial activation in anterior and posterior cingulate, striatum, frontal, temporal, parietal, and occipital cortical regions compared with the controls. The PD subjects also showed a statistically significant increase in microglial activation in temporal, parietal, and occipital regions. [11C]PIB uptake was marginally increased in PDD and PD. There was a significant reduction in glucose metabolism in PDD and PD. We have also demonstrated pixel-by-pixel correlation between mini-mental state examination (MMSE) score and microglial activation, and MMSE score and rCMRGlc. In conclusion, we have demonstrated that cortical microglial activation and reduced glucose metabolism can be detected early on in this disease spectrum. Significant microglial activation may be a factor in driving the disease process in PDD. Given this, agents that affect microglial activation could have an influence on disease progression.

Optimization of supervised cluster analysis for extracting reference tissue input curves in (R)-[11C]PK11195 brain PET studies

Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[11C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimers disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMV b ). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (V b ) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V b in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V b within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[11C]PK11195 neurodegeneration studies.

THE APPLICATION OF PET TO QUALITY ASSURANCE OF HEAVY-ION TUMOR THERAPY

SummaryAt the new heavy ion tumor therapy facility of the Gesellschaft für Schwerionenforschung at Darmstadt positron emission tomography (PET) has been implemented for in-beam and in-situ therapy control, i. e. during the tumor irradiation. The components necessary for this dedicated PET-imaging and their integration into the framework of therapy planning and quality assurance of heavy ion cancer treatments are presented. Results of the first application of this PET-method to patient treatments are reported.

Balancing bias, reliability, noise properties and the need for parametric maps in quantitative ligand PET: [11C]diprenorphine test–retest data

[(11)C]diprenorphine (DPN) is a non-subtype selective opioid receptor PET ligand with slow kinetics and no region devoid of specific binding. Parametric maps are desirable but have to overcome high noise at the voxel level. We obtained parameter values, parametric map image quality, test-retest reproducibility and reliability (using intraclass correlation coefficients (ICCs)) for conventional spectral analysis and a derived method (rank shaping), compared them with values obtained through sampling of volumes of interest (VOIs) on the dynamic data sets and tested whether smaller amounts of radioactivity injected maintained reliability. Ten subjects were injected twice with either approximately 185 MBq or approximately 135 MBq of [(11)C]DPN, followed by dynamic PET for 90 min. Data were movement corrected with a frame-to-frame co-registration method. Arterial plasma input functions corrected for radiolabelled metabolites were created. There was no overall effect of movement correction except for one subject with substantial movement whose test-retest differences decreased by approximately 50%. Actual parametric values depended heavily on the cutoff for slow frequencies (between 0.0008 s(-1) and 0.00063 s(-1)). Image quality was satisfactory for restricted base ranges when using conventional spectral analysis. The rank shaping method allowed maximising of this range but had similar bias. VOI-based methods had the widest dynamic range between regions. Average percentage test-retest differences were smallest for the parametric maps with restricted base ranges; similarly ICCs were highest for these (up to 0.86) but unacceptably low for VOI-derived VD estimates at the low doses of injected radioactivity (0.24/0.04). Our data can inform the choice of methodology for a given biological problem.

Multi-resolution Bayesian regression in PET dynamic studies using wavelets

In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new computational procedure for the production of parametric maps. An organ and, in the instance considered here, the brain presents similarities in the physiological properties of its elements across scales: computationally, this similarity can be implemented in two stages. Firstly, a multi-scale decomposition of the dynamic frames is created through the wavelet transform. Secondly, kinetic analysis is performed in wavelet space and the kinetic parameters estimated at low resolution are used as priors to inform estimates at higher resolutions. Kinetic analysis in the above scheme is achieved by extension of the Patlak analysis through Bayesian linear regression that retains the simplicity and speed of the original procedure. Application to artificial and real data (FDG and FDOPA) demonstrates the ability of the procedure to reduce remarkably the variance of parametric maps (up to 4-fold reduction) without introducing sizeable bias. Significance of the methodology and extension of the procedure to other data (fMRI) and models are discussed.

Detection and Quantification of Large-Vessel Inflammation with 11C-(R)-PK11195 PET/CT

We investigated whether PET/CT angiography using 11C-(R)-PK11195, a selective ligand for the translocator protein (18 kDa) expressed in activated macrophages, could allow imaging and quantification of arterial wall inflammation in patients with large-vessel vasculitis. Methods: Seven patients with systemic inflammatory disorders (3 symptomatic patients with clinical suspicion of active vasculitis and 4 asymptomatic patients) underwent PET with 11C-(R)-PK11195 and CT angiography to colocalize arterial wall uptake of 11C-(R)-PK11195. Tissue regions of interest were defined in bone marrow, lung parenchyma, wall of the ascending aorta, aortic arch, and descending aorta. Blood-derived and image-derived input functions (IFs) were generated. A reversible 1-tissue compartment with 2 kinetic rate constants and a fractional blood volume term were used to fit the time–activity curves to calculate total volume of distribution (VT). The correlation between VT and standardized uptake values was assessed. Results: VT was significantly higher in symptomatic than in asymptomatic patients using both image-derived total plasma IF (0.55 ± 0.15 vs. 0.27 ± 0.12, P = 0.009) and image-derived parent plasma IF (1.40 ± 0.50 vs. 0.58 ± 0.25, P = 0.018). A good correlation was observed between VT and standardized uptake value (R = 0.79; P = 0.03). Conclusion: 11C-(R)-PK11195 imaging allows visualization of macrophage infiltration in inflamed arterial walls. Tracer uptake can be quantified with image-derived IF without the need for metabolite corrections and evaluated semiquantitatively with standardized uptake values.