We measured count rates and scatter fraction on the Discovery STE PET/CT scanner in conventional 2D and 3D acquisition modes, and in a partial collimation mode between 2D and 3D. As part of the evaluation of using partial collimation, we estimated global count rates using a scanner model that combined computer simulations with an empirical live-time function. Our measurements followed the NEMA NU2 count rate and scatter-fraction protocol to obtain true, scattered and random coincidence events, from which noise equivalent count (NEC) rates were calculated. The effect of patient size was considered by using 27 cm and 35 cm diameter phantoms, in addition to the standard 20 cm diameter cylindrical count-rate phantom. Using the scanner model, we evaluated two partial collimation cases: removing half of the septa (2.5D) and removing two-thirds of the septa (2.7D). Based on predictions of the model, a 2.7D collimator was constructed. Count rates and scatter fractions were then measured in 2D, 2.7D and 3D. The scanner model predicted relative NEC variation with activity, as confirmed by measurements. The measured 2.7D NEC was equal or greater than 3D NEC for all activity levels in the 27 cm and 35 cm phantoms. In the 20 cm phantom, 3D NEC was somewhat higher (~15%) than 2.7D NEC at 100 MBq. For all higher activity concentrations, 2.7D NEC was greater and peaked 26% above the 3D peak NEC. The peak NEC in 2.7D mode occurred at ~425 MBq, and was 26-50% greater than the peak 3D NEC, depending on object size. NEC in 2D was considerably lower, except at relatively high activity concentrations. Partial collimation shows promise for improved noise equivalent count rates in clinical imaging without altering other detector parameters.
This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.
We present a simulation study of the global count-rate performance of a positron emission tomography (PET) scanner with different levels of partial collimation to maximize the noise equivalent count rate for whole-body PET imaging. We achieve partial collimation by removing different numbers of septal rings from the standard 2-D septa set for the GE Advance PET scanner. System behavior is studied with a photon tracking simulation package, which we modify to enable the production of random coincidences. The simulations are validated with measured data taken in 2-D and fully 3-D acquisition mode on a GE Advance system using the National Electrical Manufacturers Association NU-2 count-rate phantom with two sets of annular sleeves to expand the diameter to 27 and 35 cm. For all diameters and in 2-D and fully 3-D mode, there is good agreement between measurements and simulations. All studies use the three phantom diameters to evaluate the effect of patient thickness for each amount of collimation. Optimized system parameters, such as maximum ring difference for single slice rebinning, are determined for the five partially collimated systems considered. The resulting global count rates for true, scattered, and random coincidences, the noise equivalent count (NEC) rates, and the scatter fractions for different levels of collimation are compared along with the results from the conventional 2-D and fully 3-D modes. Improved statistical data quality relative to both 2-D and fully 3-D data is found with the partially collimated systems, particularly when one-half or twothirds of the septal rings are removed. An increase in NEC rates of as much as 50% is found for clinically relevant activities between 5-10 mCi (184-370 MBq). Scatter fractions for the partially collimated systems are intermediate between the 2-D and fully 3-D numbers. Many factors that affect image quality have not been considered in this paper. However, the significant increase in
We investigated the use of partial collimation on a clinical PET scanner by removing septa from conventional 2D collimators. The goal is to improve noise equivalent count-rates (NEC) compared to 2D and 3D scans for clinically relevant activity concentrations. We evaluated two cases: removing half of the septa (2.5D); and removing two-thirds of the septa (2.7D). System performance was first modeled using the SimSET simulation package, and then measured with the NEMA NU2-2001 count-rate cylinder (20 cm dia., 70 cm long), and 27 cm and 35 cm diameter cylinders of the same length. An image quality phantom was also imaged with the 2.7D collimator. SimSET predicted the relative NEC curves very well, as confirmed by measurements, with 2.5D and 2.7D NEC greater than 2D and 3D NEC in the range of ~5-20 mCi in the phantom. We successfully reconstructed images of the image quality phantom from measured 2.7D data using custom 2.7D normalization. Partial collimation shows promise for optimized clinical imaging in a fixed-collimator system.
We present a study that introduces two approaches to implementing block detectors into SimSET and compares their performance. SimSET is a photon tracking simulation package, which currently incorporates only detectors made of a solid annulus of scinitillator material. A pseudo-block approximation has been imposed on the solid annulus of conventional SimSET by discarding interactions in annulus segments that span the angular block gap. This yields blocks that are annulus segments, not rectangles. This is a quick and easy approximation of block structure, which brings SimSET results closer to actual scanner measurements. Even better agreement is expected with a deeper modification of the SimSET code that implements true rectangular blocks in the detector module (to be released late 2007/early 2008). This approach enables the greatest amount of variability and trueness to detail.We compare results from both block structure implementations to the conventional SimSET results and to measurements from a GE DSTE PET/CT scanner. Differences are evaluated in terms of sensitivities, crystal maps, and energy spectra, as well as in benchmark time tests of the simulation runs and their ease of use.Either implementation of block structure can aid in improving simulation accuracy by ameliorating one known cause of discrepancies, the geometric nature of the block detectors.
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