Impact of improved dead time correction on the quantification accuracy of a dedicated BrainPET scanner.

OBJECTIVE: Quantitative values derived from PET brain images are of high interest for neuroscientific applications. Insufficient DT correction (DTC) can lead to a systematic bias of the output parameters obtained by a detailed analysis of the time activity curves (TACs). The DTC method currently used for the Siemens 3T MR BrainPET insert is global, i.e., differences in DT losses between detector blocks are not considered, leading to inaccurate DTC and, consequently, to inaccurate measurements masked by a bias. However, following careful evaluation with phantom measurements, a new block-pairwise DTC method has demonstrated a higher degree of accuracy compared to the global DTC method. APPROACH: Differences between the global and the block-pairwise DTC method were studied in this work by applying several radioactive tracers. We evaluated the impact on [11C]ABP688, O-(2-[18F]fluoroethyl)-L-tyrosine (FET), and [15O]H2O TACs. RESULTS: For [11C]ABP688, a relevant bias of between -0.0034 and -0.0053 ml/ (cm3 • min) was found in all studied brain regions for the volume of distribution (VT) when using the current global DTC method. For [18F]FET-PET, differences of up to 10% were observed in the tumor-to-brain ratio (TBRmax), these differences depend on the radial distance of the maximum from the PET isocenter. For [15O]H2O, differences between +4% and -7% were observed in the GM region. Average biases of -4.58%, -3.2%, and -1.2% for the regional cerebral blood flow (CBF (K1)), the rate constant k2, and the volume of distribution VT were observed, respectively. Conversely, in the white matter region, average biases of -4.9%, -7.0%, and 3.8% were observed for CBF (K1), k2, and VT, respectively. CONCLUSION: The bias introduced by the global DTC method leads to an overestimation in the studied quantitative parameters for all applications compared to the block-pairwise method. SIGNIFICANCE: The observed differences between the two DTC methods are particularly relevant for research applications in neuroscientific studies as they affect the accuracy of quantitative Brain PET images.

Analysis of New Biomarkers for the Study of Schizophrenia Following a Radiomics Approach on MR and PET Imaging.

Traditionally, the diagnosis of schizophrenia was based on the psychiatrist's introspective diagnosis through clinical stratification factors and score-scales, which led to heterogeneity and discrepancy in the symptoms and results. However, there are many studies trying to improve and assist in how its diagnosis could be performed. To objectively classify schizophrenia patients it is required to determine quantitative biomarkers of the disease. In this contribution we propose a method based on feature extraction both in magnetic resonance (MR) and Positron Emission Tomography (PET) imaging. A dataset of 34 participants (17 patients and 17 control subjects) were analyzed and 5 different brain regions were studied (frontal cortex, posterior cingulate cortex, temporal cortex, primary auditory cortex and thalamus). Following a radiomics approach, 43 texture features were extracted using five different statistical methods. These features were used for the training of the five different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naive Bayes). The precision results were obtained classifying schizophrenia both in MR images (89% Area Under the Curve (AUC) in the posterior cingulate cortex) and with PET images (82% AUC in the frontal cortex), being Linear SVM and Naive Bayes the classification models with the highest predictive power. Clinical Relevance- The current study establishes a methodology to classify schizophrenia disease based on quantitative biomarkers using MR and PET images. This tool could assist the psychiatrist as an additional criterion for the diagnosis evaluation.

A detector block-pairwise dead time correction method for improved quantitation with a dedicated BrainPET scanner.

'Objective. Dead time correction (DTC) is an important factor in ensuring accurate quantification in PET measurements. This is currently often achieved using a global DTC method, i.e., an average DTC factor is computed. For PET scanners designed to image dedicated organs, e.g., those used in brain imaging or positron emission mammography (PEM), a substantial amount of the administered radioactivity is located outside of the PET field-of-view (FOV). This activity contributes to the dead time (DT) of the scintillation detectors. Moreover, the count rates of the individual scintillation detectors are potentially very inhomogeneous due to the specific irradiation of each detector, especially for combined MR/PET systems, where radiation shields cannot be applied. Approach: We have developed a block-pairwise DTC method for our Siemens 3T MR BrainPET insert by extending a previously published method that uses the delayed random coincidence count rate to estimate the DT in the individual scans and planes (i.e., scintillation pixel rings). The method was validated in decay experiments using phantoms with a homogenous activity concentration and with and without out-of-FOV activity. Based on a three-compartment phantom, we compared the accuracy and noise properties of the block-pairwise DTC and the global DTC method.Main results. The currently used global DTC led to a substantial positive bias in regions with high activity; the block-pairwise DTC resulted in substantially less bias. The noise level for the block-pairwise DTC was comparable to the global DTC and image reconstructions without any DTC. Finally, we tested the block-pairwise DTC with a data set obtained from volunteer measurements using the mGluR5 (metabotropic glutamate receptor subtype 5) antagonist [11C]ABP688. When the relative differences in activity concentrations obtained with global DTC and block-pairwise DTC for the ACC and the cerebellum GM were compared, the ratios differed by a factor of up to 1.4 at the beginning-when the first injection is administered as a bolus with high radioactivity.Significance. In this work, global DTC was shown to have the potential to introduce quantification bias, while better quantitation accuracy was achieved with the presented block-pairwise DTC method. The method can be implemented in all systems that use the delayed window technique and is particulary expected to improve the quantiation accuracy of dedicated brain PET scanners due to their geometry.'

Bias evaluation and reduction in 3D OP-OSEM reconstruction in dynamic equilibrium PET studies with 11C-labeled for binding potential analysis.

Iterative image reconstruction is widely used in positron emission tomography. However, it is known to contribute to quantitation bias and is particularly pronounced during dynamic studies with 11C-labeled radiotracers where count rates become low towards the end of the acquisition. As the strength of the quantitation bias depends on the counts in the reconstructed frame, it can differ from frame to frame of the acquisition. This is especially relevant in the case of neuro-receptor studies with simultaneous PET/MR when a bolus-infusion protocol is applied to allow the comparison of pre- and post-task effects. Here, count dependent changes in quantitation bias may interfere with task changes. We evaluated the impact of different framing schemes on quantitation bias and its propagation into binding potential (BP) using a phantom decay study with 11C and 3D OP-OSEM. Further, we propose a framing scheme that keeps the true counts per frame constant over the acquisition time as constant framing schemes and conventional increasing framing schemes are unlikely to achieve stable bias values during the acquisition time range. For a constant framing scheme with 5 minutes frames, the BP bias was 7.13±2.01% (10.8% to 3.8%) compared to 5.63±2.85% (7.8% to 4.0%) for conventional increasing framing schemes. Using the proposed constant true counts framing scheme, a stabilization of the BP bias was achieved at 2.56±3.92% (3.5% to 1.7%). The change in BP bias was further studied by evaluating the linear slope during the acquisition time interval. The lowest slope values were observed in the constant true counts framing scheme. The constant true counts framing scheme was effective for BP bias stabilization at relevant activity and time ranges. The mean BP bias under these conditions was 2.56±3.92%, which represents the lower limit for the detection of changes in BP during equilibrium and is especially important in the case of cognitive tasks where the expected changes are low.

A Linearized Fit Model for Robust Shape Parameterization of FET-PET TACs.

The kinetic analysis of [Formula: see text]-FET time-activity curves (TAC) can provide valuable diagnostic information in glioma patients. The analysis is most often limited to the average TAC over a large tissue volume and is normally assessed by visual inspection or by evaluating the time-to-peak and linear slope during the late uptake phase. Here, we derived and validated a linearized model for TACs of [Formula: see text]-FET in dynamic PET scans. Emphasis was put on the robustness of the numerical parameters and how reliably automatic voxel-wise analysis of TAC kinetics was possible. The diagnostic performance of the extracted shape parameters for the discrimination between isocitrate dehydrogenase (IDH) wildtype (wt) and IDH-mutant (mut) glioma was assessed by receiver-operating characteristic in a group of 33 adult glioma patients. A high agreement between the adjusted model and measured TACs could be obtained and relative, estimated parameter uncertainties were small. The best differentiation between IDH-wt and IDH-mut gliomas was achieved with the linearized model fitted to the averaged TAC values from dynamic FET PET data in the time interval 4-50 min p.i.. When limiting the acquisition time to 20-40 min p.i., classification accuracy was only slightly lower (-3%) and was comparable to classification based on linear fits in this time interval. Voxel-wise fitting was possible within a computation time ≈ 1 min per image slice. Parameter uncertainties smaller than 80% for all fits with the linearized model were achieved. The agreement of best-fit parameters when comparing voxel-wise fits and fits of averaged TACs was very high (p < 0.001).

TRIMAGE: A dedicated trimodality (PET/MR/EEG) imaging tool for schizophrenia.

Simultaneous PET/MR/EEG (Positron Emission Tomography - Magnetic Resonance - Electroencephalography), a new tool for the investigation of neuronal networks in the human brain, is presented here within the framework of the European Union Project TRIMAGE. The trimodal, cost-effective PET/MR/EEG imaging tool makes use of cutting edge technology both in PET and in MR fields. A novel type of magnet (1.5T, non-cryogenic) has been built together with a PET scanner that makes use of the most advanced photodetectors (i.e., SiPM matrices), scintillators matrices (LYSO) and digital electronics. The combined PET/MR/EEG system is dedicated to brain imaging and has an inner diameter of 260 mm and an axial Field-of-View of 160 mm. It enables the acquisition and assessment of molecular metabolic information with high spatial and temporal resolution in a given brain simultaneously. The dopaminergic system and the glutamatergic system in schizophrenic patients are investigated via PET, the same physiological/pathophysiological conditions with regard to functional connectivity, via fMRI, and its electrophysiological signature via EEG. In addition to basic neuroscience questions addressing neurovascular-metabolic coupling, this new methodology lays the foundation for individual physiological and pathological fingerprints for a wide research field addressing healthy aging, gender effects, plasticity and different psychiatric and neurological diseases. The preliminary performances of two components of the imaging tool (PET and MR) are discussed. Initial results of the search of possible candidates for suitable schizophrenia biomarkers are also presented as obtained with PET/MR systems available to the collaboration.

Impact evaluation of the geometry on measurements of solid radioactive waste exposure rates in nuclear medicine

INTRODUCTION: The objective of this paper is to verify the influence of the source geometry on Geiger Müller (GM) exposure rate data. This paper presents a validation of an application based on Monte Carlo (MC) data simulated using Geant4, based on a comparison of the exposure rates calculated via MC and Deterministic Calculations (DC) to experimental (measured) exposure rates. METHODS: Experimental data that were collected through measurements of standard sources were used for MC and DC validation. In addition, the best method of analyzing the impact of the real source geometry on calculations of a descarpack box of radioactive waste was verified. Furthermore, were estimated the exposure rates from a homogeneous solid waste box (used at clinical sites) and from a point source. These results were compared to confirm possible discrepancies related to source geometry in exposure rates collected using a GM detector. RESULTS: The investigated estimation methods were statistically compared; the MC presented higher agreement with the experimental data than did the deterministic calculations. The impact of considering a point source instead of the real geometry (descarpack box) was an underestimation of between 20% and 70%, depending on the source - detector distance and the isotope evaluated. CONCLUSION: The DC always presented a higher difference with respect to the experimental data than did the MC calculation. The use of realistic geometry proved to exert a significant impact on the exposure rate data for solid radioactive waste compared with the exposure rate induced by a point source; the exposure rate estimation obtained using the real geometry was always at least 16% higher than the estimation obtained for a point source, and some differences greater than 50% were found.

Ambiente colaborativo para formação de pessoal em medicina nuclear / Collaborative environment for nuclear medicine training

OBJETIVO: Validar a proposta do desenvolvimento de um ambiente colaborativo virtual para formação de pessoal em medicina nuclear. MATERIAIS E MÉTODOS: No desenvolvimento inicial do ambiente foram levantadas as premissas, restrições e funcionalidades que deveriam ser oferecidas aos profissionais da área. O protótipo foi desenvolvido no ambiente Moodle, incluindo funcionalidades de armazenamento de dados e interação. Um estudo piloto de interação no ambiente foi realizado com uma amostra de profissionais especialistas em medicina nuclear. Análises quantitativas e de conteúdo foram realizadas a partir de um questionário semiestruturado de opinião dos usuários. RESULTADOS: A proposta do ambiente colaborativo foi validada por uma comunidade de profissionais que atuam nesta área e considerada relevante visando a auxiliar na formação de pessoal. Sugestões de melhorias e novas funcionalidades foram indicadas. Observou-se a necessidade de estabelecer um programa de formação dos moderadores no ambiente, visto que são necessárias características de interação distintas do ensino presencial. CONCLUSÃO: O ambiente colaborativo poderá permitir a troca de experiências e a discussão de casos entre profissionais localizados em instituições de diferentes regiões do País, possibilitando uma aproximação e colaboração entre esses profissionais. Assim, o ambiente pode contribuir para formação inicial e continuada de profissionais que atuam em medicina nuclear.

OBJECTIVE: To validate the proposal for development of a virtual collaborative environment for training of nuclear medicine personnel. MATERIALS AND METHODS: Organizational assumptions, constraints and functionalities that should be offered to the professionals in this field were raised early in the development of the environment. The prototype was developed in the Moodle environment, including data storage and interaction functionalities. A pilot interaction study was developed with a sample of specialists in nuclear medicine. Users' opinions collected by means of semi-structured questionnaire were submitted to quantitative and content analysis. RESULTS: The proposal of a collaborative environment was validated by a community of nuclear medicine professionals and considered as an aid in the training in this field. Suggestions for improvements and new functionalities were made. There is a need to establish a program for education of moderators specifically for this environment, considering the different interaction characteristics as the online and conventional teaching methods are compared. CONCLUSION: The collaborative environment will allow the exchange of experiences and case discussions among professionals from institutions located in different regions all over the country, enhancing the collaboration among them. Thus, the environment can contribute in the early and continued education of nuclear medicine professionals.