Brian F. Elston

A Digital Reference Object to Analyze Calculation Accuracy of PET Standardized Uptake Value.

PURPOSE To determine the extent of variations in computing standardized uptake value (SUV) by body weight (SUV(BW)) among different software packages and to propose a Digital Imaging and Communications in Medicine (DICOM) reference test object to ensure the standardization of SUV computation between medical image viewing workstations. MATERIALS AND METHODS Research ethics board approval was not necessary because this study only evaluated images of a phantom. A synthetic set of positron emission tomographic (PET)/computed tomographic (CT) image data, called a digital reference object (DRO), with known SUV was created. The DRO was sent to 16 sites and evaluated on 21 different PET/CT display software packages. Users were asked to draw various regions of interest (ROIs) on specific features and report the maximum, minimum, mean, and standard deviation of the SUVs for each ROI. Numerical tolerances were defined for each metric, and the fraction of reported values within the tolerance was recorded, as was the mean, standard deviation, and range of the metrics. RESULTS The errors in reported maximum SUV ranged from -37.8% to 0% for an isolated voxel with 4.11:1 target-to-background activity level, and errors in the reported mean SUV ranged from -1.6% to 100% for a region with controlled noise. There was also a range of errors in the less commonly used metrics of minimum SUV and standard deviation SUV. CONCLUSION The variability of computed SUV(BW) between different software packages is substantial enough to warrant the introduction of a reference standard for medical image viewing workstations.

Evaluation of Cross-Calibrated ⁶⁸Ge/⁶⁸Ga Phantoms for Assessing PET/CT Measurement Bias in Oncology Imaging for Single- and Multicenter Trials

Quantitative PET imaging is an important tool for clinical trials evaluating the response of cancers to investigational therapies. The standardized uptake value, used as a quantitative imaging biomarker, is dependent on multiple parameters that may contribute bias and variability. The use of long-lived, sealed PET calibration phantoms offers the advantages of known radioactivity activity concentration and simpler use than aqueous phantoms. We evaluated scanner and dose calibrator sources from two batches of commercially available kits, together at a single site and distributed across a local multicenter PET imaging network. We found that radioactivity concentration was uniform within the phantoms. Within the regions of interest drawn in the phantom images, coefficients of variation of voxel values were less than 2%. Across phantoms, coefficients of variation for mean signal were close to 1%. Biases of the standardized uptake value estimated with the kits varied by site and were seen to change in time by approximately ±5%. We conclude that these biases cannot be assumed constant over time. The kits provide a robust method to monitor PET scanner and dose calibrator biases, and resulting biases in standardized uptake values.

An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms

Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU‐2 Image Quality (IQ) phantom is a time‐consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open‐source plug‐in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows runtimes between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU‐2 IQ phantom for PET/CT scanner acceptance testing and QA/QC. PACS number: 87.57.C‐Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows runtimes between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC. PACS number: 87.57.C.

Simulation study for designing a compact brain PET scanner

Summary form only given. The desire to understand normal and disordered human brain of upright, moving persons in natural environments motivates the development of an ambulatory micro-dose brain PET imager (AMPET) [1]. An ideal system would be light weight and have high sensitivity and spatial resolution. These requirements are often in conflict with each other. Therefore, we performed simulation studies to search for the optimal system configuration and to evaluate the improvement in performance over existing scanners. An intuitive design to achieve high sensitivity is to use a tight geometry that covers the brain. However, a tight geometry also increases parallax error in peripheral lines of response, which may increase the variance in ROI quantification. In this study, we first simulated cylindrical PET with different ring diameters. All PET configurations are subjected to the same maximum weight constraint by restricting the amount of detector materials. We computed the Cramér-Rao variance bound, which is the lower bound of the variance for an unbiased estimator, to compare the performance for region of interest (ROI) quantification using different scanner geometries. The results show that while a smaller ring diameter can increase photon detection sensitivity and hence reduce the variance in the center of the field of view, it can result in higher pixel variance in peripheral regions when the length of detector crystal is 15 mm or more. The variance can be substantially reduced by adding depth of interaction (DOI) measurements to the detectors. Our simulation study also shows that the relative performance highly depends on the size of the ROI, and a large ROI favors a tighter geometry even without DOI information. Based on the 2D simulation results, we proposed a helmet scanner design with DOI detectors as shown in Fig. 1, which is similar to the design in [2]. This helmet scanner consists of three parts: a top panel, side rings with varying diameters, and a bottom panel. We used the Siemens brain MR-PET scanner geometry as a reference for comparison. The detector block parameters and the diameter of the bottom ring for the helmet scanner are the same as the reference cylinder scanner. Parameters of the side rings of the helmet scanner are listed in Table I. The bottom panel contains 4×4 detector blocks and the top panel contains 52 detector blocks. Distance between the bottom flat panel and the bottom ring is about 160 mm and the axial gap between the top panel and the top ring is 2.5 mm. GATE V6.2 [3] was used to perform Monte Carlo simulations. GATE simulation results of the cylindrical scanner and the helmet with side rings only were cross-validated by SimSET simulation results [4]. The results showed that the sensitivity of the helmet scanner is about 4 times that of the reference cylindrical scanner. The sensitivity improvement is also position dependent. The bottom panel mainly improves the sensitivity in the lower portion of the scanner FOV, while the top panel mainly improves the sensitivity in the upper portion of the FOV. The maximum improvement is near the top with a gain factor up to 35. Reconstructions of the simulated Hoffman phantom [5] data showed that the helmet scanner can substantially improve the image quality over the reference cylindrical scanner.

Spatial covariance characteristics in a collection of 3-D PET scanners used in clinical imaging trials

An NCI-sponsored program to allow qualification of PET imaging sites for use in Cancer Clinical trials has created a data set, assembled and maintained by the American College of Radiology Imaging Network (ACRIN), with a rich collection of PET phantom measurements assessing imaging quality. Previous work with the data has focused on systematic deviations between the actual activity value in the phantom and its measurement by an average of PET-recorded voxel values in the region of interest corresponding to the phantom in the scanner. But the data also allow for more detailed evaluation of imaging characteristics. Our work focuses on components of random variation. Dynamic data considered are obtained from 3-D sequential scanning, typically with variations on OSEM for reconstruction, of a uniform cylindrical phantom over a 25 minute period. We evaluate axial, transaxial and temporal patterns in variance and covariance. Variance characteristics are dominated by effective counts - these are lower over short time-frames and in the axial extremes of the scanner bed. After adjustment for variance the spatial auto-correlation patterns in 3-D are evaluated. Auto-correlation is decomposed as a product of axial and trans-axial effects. The trans-axial pattern follows the structure generally associated with standard 2-D filtered back-projection reconstruction - i.e. largely determined by the impulse response. Having a phantom based measurement of the variance and auto-correlation patterns gives the possibility to make more efficient use of region of interest data from patient scans. Usual regional averages can be replaced by weighted averages, with weights inversely proportional to the local variance. In addition, an approximate standard error for an ROI average can take account of the phantom derived measurement of the auto-correlations. Thus information from routine phantom scanning would practially enhance the value of information recovered for patient studies.

A digital reference object for the 3D Hoffman brain phantom for characterization of PET neuroimaging quality

We have developed a digital reference object that faithfully represents the structures in the Hoffman 3-D Brain Phantom™ from Data Spectrum Corporation. We have tested the fidelity of the DRO to the phantom by comparing the DRO to a CT scan, finding excellent agreement. We have also compared a PET simulation of the DRO to a PET scan of the phantom. The agreement between the simulated DRO and phantom PET scans was good.