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.

Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI.

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine.

Dual-time-point O-(2-[(18)F]fluoroethyl)-L-tyrosine PET for grading of cerebral gliomas.

OBJECTIVE: We aimed to evaluate the diagnostic potential of dual-time-point imaging with positron emission tomography (PET) using O-(2-[(18)F]fluoroethyl)-L-tyrosine ((18)F-FET) for non-invasive grading of cerebral gliomas compared with a dynamic approach. METHODS: Thirty-six patients with histologically confirmed cerebral gliomas (21 primary, 15 recurrent; 24 high-grade, 12 low-grade) underwent dynamic PET from 0 to 50 min post-injection (p.i.) of (18)F-FET, and additionally from 70 to 90 min p.i. Mean tumour-to-brain ratios (TBRmean) of (18)F-FET uptake were determined in early (20-40 min p.i.) and late (70-90 min p.i.) examinations. Time-activity curves (TAC) of the tumours from 0 to 50 min after injection were assigned to different patterns. The diagnostic accuracy of changes of (18)F-FET uptake between early and late examinations for tumour grading was compared to that of curve pattern analysis from 0 to 50 min p.i. of (18)F-FET. RESULTS: The diagnostic accuracy of changes of the TBRmean of (18)F-FET PET uptake between early and late examinations for the identification of HGG was 81% (sensitivity 83%; specificity 75%; cutoff - 8%; p < 0.001), and 83% for curve pattern analysis (sensitivity 88%; specificity 75%; p < 0.001). CONCLUSION: Dual-time-point imaging of (18)F-FET uptake in gliomas achieves diagnostic accuracy for tumour grading that is similar to the more time-consuming dynamic data acquisition protocol. KEY POINTS: • Dual-time-point imaging is equivalent to dynamic FET PET for grading of gliomas. • Dual-time-point imaging is less time consuming than dynamic FET PET. • Costs can be reduced due to higher patient throughput. • Reduced imaging time increases patient comfort and sedation might be avoided. • Quicker image interpretation is possible, as no curve evaluation is necessary.

Relationship of regional cerebral blood flow and kinetic behaviour of O-(2-(18)F-fluoroethyl)-L-tyrosine uptake in cerebral gliomas.

OBJECTIVES: O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) is an established tracer for brain tumour imaging. (18)F-FET kinetics in gliomas appear to have potential for tumour grading, but the mechanisms remain unclear. The aim of this study was to explore the relationship between regional cerebral blood flow (rCBF) as measured by arterial spin labelling MRI and the kinetic behaviour of (18)F-FET PET in cerebral gliomas. MATERIALS AND METHODS: Twenty patients with cerebral gliomas were investigated using arterial spin labelling MRI and dynamic (18)F-FET PET. Time-activity curves (TACs) of (18)F-FET uptake were analysed in 33 different tumour regions. The slopes of TAC during the early (0-5 min; slopeup) and late phases of tracer uptake (17-50 min; slopedown) were fitted using linear regression lines. In addition, TACs of each lesion were assigned to different curve patterns. Furthermore, we calculated tumour-to-brain ratios of (18)F-FET uptake. The relationship between (18)F-FET parameters and rCBF was determined. RESULTS: (18)F-FET uptake in the early phase (slopeup) showed a significant correlation with rCBF (r=0.4; P=0.02). In contrast, both slopedown and TAC patterns showed no significant correlation with rCBF. Furthermore, a significant correlation was found between rCBF and tumour-to-brain ratio (r=0.53; P=0.002). CONCLUSION: There is a relationship between rCBF and (18)F-FET uptake in cerebral gliomas in the initial uptake phase, but the kinetic behaviour of (18)F-FET uptake in the late phase is not significantly influenced by rCBF. Thus, the differential kinetic pattern of (18)F-FET uptake in high-grade and low-grade gliomas appears to be determined by factors other than rCBF.

Advances in multimodal neuroimaging: hybrid MR-PET and MR-PET-EEG at 3 T and 9.4 T.

Multi-modal MR-PET-EEG data acquisition in simultaneous mode confers a number of advantages at 3 T and 9.4 T. The three modalities complement each other well; structural-functional imaging being the domain of MRI, molecular imaging with specific tracers is the strength of PET, and EEG provides a temporal dimension where the other two modalities are weak. The utility of hybrid MR-PET at 3 T in a clinical setting is presented and critically discussed. The potential problems and the putative gains to be accrued from hybrid imaging at 9.4 T, with examples from the human brain, are outlined. Steps on the road to 9.4 T multi-modal MR-PET-EEG are also illustrated. From an MR perspective, the potential for ultra-high resolution structural imaging is discussed and example images of the cerebellum with an isotropic resolution of 320 µm are presented, setting the stage for hybrid imaging at ultra-high field. Further, metabolic imaging is discussed and high-resolution images of the sodium distribution are presented. Examples of tumour imaging on a 3 T MR-PET system are presented and discussed. Finally, the perspectives for multi-modal imaging are discussed based on two on-going studies, the first comparing MR and PET methods for the measurement of perfusion and the second which looks at tumour delineation based on MRI contrasts but the knowledge of tumour extent is based on simultaneously acquired PET data.