Cerebral blood flow and glucose metabolism in healthy volunteers measured using a high-resolution PET scanner

BACKGROUND: Positron emission tomography (PET) allows for the measurement of cerebral blood flow (CBF; based on [(15)O]H(2)O) and cerebral metabolic rate of glucose utilization (CMR(glu); based on [(18) F]-2-fluoro-2-deoxy-d-glucose ([(18) F]FDG)). By using kinetic modeling, quantitative CBF and CMR(glu) values can be obtained. However, hardware limitations led to the development of semiquantitive calculation schemes which are still widely used. In this paper, the analysis of CMR(glu) and CBF scans, acquired on a current state-of-the-art PET brain scanner, is presented. In particular, the correspondence between nonlinear as well as linearized methods for the determination of CBF and CMR(glu) is investigated. As a further step towards widespread clinical applicability, the use of an image-derived input function (IDIF) is investigated. METHODS: Thirteen healthy male volunteers were included in this study. Each subject had one scanning session in the fasting state, consisting of a dynamic [(15)O]H(2)O scan and a dynamic [(18) F]FDG PET scan, acquired at a high-resolution research tomograph. Time-activity curves (TACs) were generated for automatically delineated and for manually drawn gray matter (GM) and white matter regions. Input functions were derived using on-line arterial blood sampling (blood sampler derived input function (BSIF)). Additionally, the possibility of using carotid artery IDIFs was investigated. Data were analyzed using nonlinear regression (NLR) of regional TACs and parametric methods. RESULTS: After quality control, 9 CMR(glu) and 11 CBF scans were available for analysis. Average GM CMR(glu) values were 0.33 ± 0.04 μmol/cm(3) per minute, and average CBF values were 0.43 ± 0.09 mL/cm(3) per minute. Good correlation between NLR and parametric CMR(glu) measurements was obtained as well as between NLR and parametric CBF values. For CMR(glu) Patlak linearization, BSIF and IDIF derived results were similar. The use of an IDIF, however, did not provide reliable CBF estimates. CONCLUSION: Nonlinear regression analysis, allowing for the derivation of regional CBF and CMR(glu) values, can be applied to data acquired with high-spatial resolution current state-of-the-art PET brain scanners. Linearized models, applied to the voxel level, resulted in comparable values. CMR(glu) measurements do not require invasive arterial sampling to define the input function. TRIAL REGISTRATION: ClinicalTrials.gov NCT0062608

FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0

The aim of this guideline is to provide a minimum standard for the acquisition and interpretation of PET and PET/CT scans with [18F]-fluorodeoxyglucose (FDG). This guideline will therefore address general information about [18F]-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and is provided to help the physician and physicist to assist to carrying out, interpret, and document quantitative FDG PET/CT examinations, but will concentrate on the optimisation of diagnostic quality and quantitative information

Optimizing an online SPE-HPLC method for analysis of (R)-[11C]1-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinolinecarb oxamide [(R)-[11C]PK11195] and its metabolites in humans

(R)-[11C]PK11195 is used as a positron emission tomography tracer for activated microglia in several neurological disorders. Quantification of specific binding requires a metabolite-corrected plasma input function. In this study, a high-performance liquid chromatography (HPLC) procedure with online solid phase extraction was modified for analyzing (R)-[11C]PK11195 plasma samples, yielding total sample recoveries of more than 98%. When applied to human studies, the use of two HPLC systems enabled analysis of up to seven plasma samples under regular conditions. Online radioactivity detection was compared with offline sample measurements of HPLC profiles. Offline measurements provided the most reliable results especially for late plasma samples. In 10 patients, an average decrease of parent compound from 94.6% at 2.5 min to 45.2% at 1 h after administration was observed

Pharmaceutical preparation of oxygen-15 labelled molecular oxygen and carbon monoxide gasses in a hospital setting

BACKGROUND: Clinical positron emission tomography (PET) requires safe and effective PET radiopharmaceuticals. Tracers used for measuring oxygen consumption and blood volume are [(15)O]O(2) and [(15)O]CO, respectively. In general, these oxygen-15 labelled tracers are produced using a cyclotron that accelerates deuterons onto a target filled with (14)N(2) containing a trace of oxygen. In recent years, cyclotrons have been developed that only are capable of accelerating protons. The purpose of this study was to validate and assess such a cyclotron for production and administration of oxygen-15 labelled gasses in an hospital setting. METHODS: An RDS111 cyclotron (Siemens-CTI, Knoxville, USA) was validated for bolus production of [(15)O]O(2) and [(15)O]CO gasses. In addition, equipment was developed to administer these tracers to patients. RESULTS: Both [(15)O]O(2) and [(15)O]CO gasses could be produced in sufficient amounts, whilst meeting European Pharmacopeia requirements. Although produced oxygen-15 gasses contained a minor level of (11)C contamination, in clinical studies it was possible to correct for this contamination by delayed blood counting. CONCLUSION: An 11 MeV proton cyclotron combined with an in-house developed gas delivery system allows for the production and administration of sufficient amounts of [(15)O]-gasses for routine clinical PET studies in an hospital setting

Quantification of FDG PET studies using standardised uptake values in multi-centre trials: effects of image reconstruction, resolution and ROI definition parameters.

Contains fulltext : 52887.pdf (publisher's version ) (Closed access)PURPOSE: Standardised uptake values (SUVs) depend on acquisition, reconstruction and region of interest (ROI) parameters. SUV quantification in multi-centre trials therefore requires standardisation of acquisition and analysis protocols. However, standardisation is difficult owing to the use of different scanners, image reconstruction and data analysis software. In this study we evaluated whether SUVs, obtained at three different institutes, may be directly compared after calibration and correction for inter-institute differences. METHODS: First, an anthropomorphic thorax phantom containing variously sized spheres and activities, simulating tumours, was scanned and processed in each institute to evaluate differences in scanner calibration. Secondly, effects of image reconstruction and ROI method on recovery coefficients were studied. Next, SUVs were derived for tumours in 23 subjects. Of these 23 patients, four and ten were scanned in two institutes on an HR+ PET scanner and nine were scanned in one institute on an ECAT EXACT PET scanner. All phantom and clinical data were reconstructed using iterative reconstruction with various iterations, with both measured (MAC) and segmented attenuation correction (SAC) and at various image resolutions. Activity concentrations (AC) or SUVs were derived using various ROI isocontours. RESULTS: Phantom data revealed differences in SUV quantification of up to 30%. After application-specific calibration, recovery coefficients obtained in each institute were equal to within 15%. Varying the ROI isocontour value resulted in a predictable change in SUV (or AC) for both phantom and clinical data. Variation of image resolution resulted in a predictable change in SUV quantification for large spheres/tumours (>5 cc) only. For smaller tumours (<2 cc), differences of up to 40% were found between high (7 mm) and low (10 mm) resolution images. Similar differences occurred when data were reconstructed with a small number of iterations. Finally, no significant differences between MAC and SAC reconstructed data were observed, except for tumours near the diaphragm. CONCLUSION: Standardisation of acquisition, reconstruction and ROI methods is preferred for SUV quantification in multi-centre trials. Small unavoidable differences in methodology can be accommodated by performing a phantom study to assess inter-institute correction factors