Cardiac Displacement During 13N-Ammonia Myocardial Perfusion PET/CT: Comparison Between Adenosine- and Regadenoson-Induced Stress.

This study investigated differences in cardiac displacement during adenosine stress versus regadenoson stress in 13N-ammonia (13NH3) MP PET/CT scans. Methods: In total, 61 myocardial perfusion PET/CT scans were acquired using either adenosine (n = 30) or regadenoson (n = 31) as a stressor. For both groups, cardiac displacement during rest and stress was measured 3-dimensionally, relative to either a fixed reference frame or the previous frame, in each 1-min frame of a list-mode PET acquisition of 25 min. All stress scans were additionally evaluated for the presence of motion artifacts. Also, the tolerability of the agents and the occurrence of side effects were compared between groups. Results: Significantly larger cardiac displacement during stress was detected in the adenosine group than in the regadenoson group, reflected by both maximal cardiac displacement (P = 0.022) and mean cardiac displacement (P = 0.001). The duration of the movement was typically shorter in the regadenoson group. Frames with cardiac displacement of at least 5 mm were observed nearly twice as frequently when adenosine was used instead of regadenoson. Conclusion: The displacement during regadenoson stress is of lower amplitude and shorter duration than that during adenosine stress and may therefore contribute to a lower incidence of motion artifacts on PET/CT scans.

Quantification of myocardial blood flow in absolute terms using (82)Rb PET imaging: the RUBY-10 Study.

OBJECTIVES: The purpose of this study was to compare myocardial blood flow (MBF) and myocardial flow reserve (MFR) estimates from rubidium-82 positron emission tomography ((82)Rb PET) data using 10 software packages (SPs) based on 8 tracer kinetic models. BACKGROUND: It is unknown how MBF and MFR values from existing SPs agree for (82)Rb PET. METHODS: Rest and stress (82)Rb PET scans of 48 patients with suspected or known coronary artery disease were analyzed in 10 centers. Each center used 1 of 10 SPs to analyze global and regional MBF using the different kinetic models implemented. Values were considered to agree if they simultaneously had an intraclass correlation coefficient >0.75 and a difference <20% of the median across all programs. RESULTS: The most common model evaluated was the Ottawa Heart Institute 1-tissue compartment model (OHI-1-TCM). MBF values from 7 of 8 SPs implementing this model agreed best. Values from 2 other models (alternative 1-TCM and Axially distributed) also agreed well, with occasional differences. The MBF results from other models (e.g., 2-TCM and retention) were less in agreement with values from OHI-1-TCM. CONCLUSIONS: SPs using the most common kinetic model-OHI-1-TCM-provided consistent results in measuring global and regional MBF values, suggesting that they may be used interchangeably to process data acquired with a common imaging protocol.

Insight on automated lesion delineation methods for PET data.

BACKGROUND: Defining tumour volume for treatment response and radiotherapy planning is challenging and prone to inter- and intra-observer variability. Various automated tumour delineation methods have been proposed in the literature, each having abilities and limitations. Therefore, there is a need to provide clinicians with practical information on delineation method selection. METHODS: Six different automated positron emission tomography (PET) delineation methods were evaluated and compared using National Electrical Manufacturer Association image quality (NEMA IQ) phantom data and three in-house synthetic phantoms with clinically relevant lesion shapes including spheres with necrotic core and irregular shapes. The impact of different contrast ratios, emission counts, realisations and reconstruction algorithms on delineation performance was also studied using similarity index (SI) and percentage volume error (%VE) as performance measures. RESULTS: With the NEMA IQ phantom, contrast thresholding (CT) performed best on average for all sphere sizes and parameter settings (SI = 0.83; %VE = 5.65% ± 24.34%). Adaptive thresholding at 40% (AT40) was the next best method and required no prior parameter tuning (SI = 0.78; %VE = 23.22% ± 70.83%). When using SUV harmonisation filtering prior to delineation (EQ.PET), AT40 remains the best method without prior parameter tuning (SI = 0.81; %VE = 11.39% ± 85.28%). For necrotic core spheres and irregular shapes of the synthetic phantoms, CT remained the best performing method (SI = 0.83; %VE = 26.31% ± 38.26% and SI = 0.62; %VE = 24.52% ± 46.89%, respectively). The second best method was fuzzy locally adaptive Bayesian (FLAB) (SI = 0.83; %VE = 29.51% ± 81.79%) for necrotic core sphere and AT40 (SI = 0.58; %VE = 25.11% ± 32.41%) for irregular shapes. When using EQ.PET prior to delineation, AT40 was the best performing method without prior parameter tuning for both necrotic core (SI = 0.83; %VE = 27.98% ± 59.58%) and complex shapes phantoms (SI = 0.61; %VE = 14.83% ± 49.39%). CONCLUSIONS: CT and AT40/AT50 are recommended for all lesion sizes and contrasts. Overall, considering background uptake information improves PET delineation accuracy. Applying EQ.PET prior to delineation improves accuracy and reduces coefficient of variation (CV) across different reconstructions and acquisitions.

SUVref: reducing reconstruction-dependent variation in PET SUV.

BACKGROUND: We propose a new methodology, reference Standardised Uptake Value (SUVref), for reducing the quantitative variation resulting from differences in reconstruction protocol. Such variation that is not directly addressed by the use of SUV or the recently proposed PERCIST can impede comparability between positron emission tomography (PET)/CT scans. METHODS: SUVref applies a reconstruction-protocol-specific phantom-optimised filter to clinical PET scans for the purpose of improving comparability of quantification. The ability of this filter to reduce variability due to differences in reconstruction protocol was assessed using both phantom and clinical data. RESULTS: SUVref reduced the variability between recovery coefficients measured with the NEMA image quality phantom across a range of reconstruction protocols to below that measured for a single reconstruction protocol. In addition, it enabled quantitative conformance to the recently proposed EANM guidelines. For the clinical data, a significant reduction in bias and variance in the distribution of differences in SUV, resulting from differences in reconstruction protocol, greatly reduced the number of hot spots that would be misclassified as undergoing a clinically significant change in SUV. CONCLUSIONS: SUVref significantly reduces reconstruction-dependent variation in SUV measurements, enabling increased confidence in quantitative comparison of clinical images for monitoring treatment response or disease progression. This new methodology could be similarly applied to reduce variability from scanner hardware.

Improving influx constant and ratio estimation in FDOPA brain PET analysis for Parkinson's disease.

UNLABELLED: PET studies using l-3,4-dihydroxy-6-(18)F-fluorophenylalanine have been applied to assess the diminished functionality of the striatum in patients with suspected Parkinson's disease. Two techniques for analyzing such studies are ratio methods and graphically computed influx constants. We propose a method for improving the consistency with which scans obtained by either of these techniques are analyzed. The method is based on a fully 3-dimensional analysis of the images. METHODS: Fifty-one dynamically acquired datasets were corrected for motion before analysis. Regions of interest (ROIs) for the analysis were determined by manual affine registration to a standard template, using a separate transformation for each ROI. Indicator values for each ROI were computed by averaging the values of voxels having the highest activity within a specified proportion of the voxels in the ROI, to increase the robustness to perturbations in the ROI position. Sensitivity was analyzed by examining the variation in results obtained when the ROIs were translated by up to 6 mm. RESULTS: We observed significant percentage differences in the computed influx constant before and after motion correction (mean variation +/- SD, -0.75% +/- 9.5% averaged over all regions of all patients). Our method was robust to placement of the cerebellum ROI, whereas a 2- dimensional analysis based on hand-drawn ROIs was associated with a 2- to 3-fold greater percentage variation in uptake for translations of 2 mm or more in ROI position. When we compared the 2 quantification techniques, our influx constants and ratios correlated at r(2) = 0.91, P < 0.0001. CONCLUSION: Motion correction is an important step for computing reliable results in dynamic studies. The robustness of the results can be increased further by using standard normalized volumetric ROIs and by using the average value of a specified proportion of the voxels with the highest activity in the ROI as an indicator for that ROI. Influx constant values computed using our analysis technique closely correlated to values computed with ratio methods using this general approach.