Kinetic modeling and parameter estimation of TSPO PET imaging in the human brain

PURPOSE: Translocator protein 18-kDa (TSPO) imaging with positron emission tomography (PET) is widely used in research studies of brain diseases that have a neuro-immune component. Quantification of TSPO PET images, however, is associated with several challenges, such as the lack of a reference region, a genetic polymorphism affecting the affinity of the ligand for TSPO, and a strong TSPO signal in the endothelium of the brain vessels. These challenges have created an ongoing debate in the field about which type of quantification is most useful and whether there is an appropriate simplified model. METHODS: This review focuses on the quantification of TSPO radioligands in the human brain. The various methods of quantification are summarized, including the gold standard of compartmental modeling with metabolite-corrected input function as well as various alternative models and non-invasive approaches. Their advantages and drawbacks are critically assessed. RESULTS AND CONCLUSIONS: Researchers employing quantification methods for TSPO should understand the advantages and limitations associated with each method. Suggestions are given to help researchers choose between these viable alternative methods

Kinetic analysis of [11C]befloxatone in the human brain, a selective radioligand to image monoamine oxidase A.

International audienceBACKGROUND: [11C]Befloxatone measures the density of the enzyme monoamine oxidase A (MAO-A) in the brain. MAO-A is responsible for the degradation of different neurotransmitters and is implicated in several neurologic and psychiatric illnesses. This study sought to estimate the distribution volume (VT) values of [11C]befloxatone in humans using an arterial input function. METHODS: Seven healthy volunteers were imaged with positron emission tomography (PET) after [11C]befloxatone injection. Kinetic analysis was performed using an arterial input function in association with compartmental modeling and with the Logan plot, multilinear analysis (MA1), and standard spectral analysis (SA) at both the regional and voxel level. Arterialized venous samples were drawn as an alternative and less invasive input function. RESULTS: An unconstrained two-compartment model reliably quantified VT values in large brain regions. A constrained model did not significantly improve VT identifiability. Similar VT results were obtained using SA; however, the Logan plot and MA1 slightly underestimated VT values (about -10 %). At the voxel level, SA showed a very small bias (+2 %) compared to compartmental modeling, Logan severely underestimated VT values, and voxel-wise images obtained with MA1 were too noisy to be reliably quantified. Arterialized venous blood samples did not provide a satisfactory alternative input function as the Logan-VT regional values were not comparable to those obtained with arterial sampling in all subjects. CONCLUSIONS: Binding of [11C]befloxatone to MAO-A can be quantified using an arterial input function and a two-compartment model or, in parametric images, with SA

Human Biodistribution and Dosimetry of 11C-CUMI-101, an Agonist Radioligand for Serotonin-1A Receptors in Brain

As a reported agonist,11C-CUMI-101 is believed to selectively bind the G-protein-coupled state of the serotonin-1A (5-HT1A) receptor, thereby providing a measure of the active subset of all 5-HT1A receptors in brain. Although 11C-CUMI-101 has been successfully used to quantify 5-HT1A receptors in human and monkey brain, its radiation exposure has not previously been reported. The purpose of this study was to calculate the radiation exposure to organs of the body based on serial whole-body imaging with positron emission tomography (PET) in human subjects

Image-Derived Input Function for Human Brain Using High Resolution PET Imaging with [11C](R)-rolipram and [11C]PBR28

The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods. values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling

EJNMMI Res

Inflammatory vascular disease of the arteries, such as inflamed atheromatous plaques or arteritis, may cause aneurysms or ischemic strokes. In this context, using positron emission tomography (PET) to image inflammation may help select patients who would benefit from appropriate therapeutic interventions. This study sought to assess the usefulness of the 18 kDa translocator protein (TSPO) tracers [C]-PBR28 and [F]-PBR06 for imaging inflammatory vascular disease in vitro and in vivo. Immunohistochemistry for macrophage infiltration as well as autoradiography with [F]-PBR06 were performed on eight paraffin-embedded, formalin-fixed atherosclerosis plaques prospectively collected after carotid endarterectomy of eight patients affected by ischemic stroke. Six different patients, one of whom was also included in the in vitro study, underwent PET imaging. Two patients with carotid stenosis associated with ischemic stroke were imaged with [F]-PBR06 PET/CT, and four other patients (three with large vessel vasculitis and one with bilateral carotid stenosis but without stroke) were imaged with [C]-PBR28. All in vitro sections showed specific binding of [F]-PBR06, which co-localized with immunohistochemistry markers for inflammation. However, in vivo TSPO imaging with either [C]-PBR28 or [F]-PBR06 was negative in all participants. Despite good uptake on surgical samples in vitro, [C]-PBR28 and [F]-PBR06 are not viable clinical tools for imaging inflammatory vascular disease. NCT02513589, registered 31 July 2015 and NCT00547976, registered 23 October 2007. https://clinicaltrials.gov

Voxelwise quantification of [11C](R)-rolipram PET data: a comparison between model-based and data-driven methods

This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [11 C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic V T estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise V T estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1\ub11%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x-0.013; R 2 =0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R 2 >0.96). H-BFM yielded the most reliable voxelwise quantification of [11 C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only V T estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness

Voxelwise quantification of [(11)C](R)-rolipram PET data:a comparison between model-based and data-driven methods

This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [(11)C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic V(T) estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise V(T) estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1±1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x−0.013; R(2)=0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R(2)>0.96). H-BFM yielded the most reliable voxelwise quantification of [(11)C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only V(T) estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness

Predicting the outcome of peptide receptor radionuclide therapy in neuroendocrine tumors: the importance of dual-tracer imaging

Letter about the article by Sansovini et al. “Sansovini M, Severi S, Ianniello A, Nicolini S, Fantini L, Mezzenga E, Ferroni F, Scarpi E, Monti M, Bongiovanni A, Cingarlini S, Grana CM, Bodei L, Paganelli G. Long-term follow-up and role of FDG PET in advanced pancreatic neuroendocrine patients treated with 177Lu-DOTATATE. Eur J Nucl Med Mol Imaging. 2017 Mar;44(3):490-499.