We examine a maximum-a-posteriori method for estimating the primary interaction position of gamma rays with multiple interaction sites (hits) in a monolithic detector. In assessing the performance of a multiple-hit estimator over that of a conventional one-hit estimator, we consider a few different detector and readout configurations of a 50-mm-wide square cerium-doped lutetium oxyorthosilicate block. For this study, we use simulated data from SCOUT, a Monte-Carlo tool for photon tracking and modeling scintillation- camera output. With this tool, we determine estimate bias and variance for a multiple-hit estimator and compare these with similar metrics for a one-hit maximum-likelihood estimator, which assumes full energy deposition in one hit. We also examine the effect of event filtering on these metrics; for this purpose, we use a likelihood threshold to reject signals that are not likely to have been produced under the assumed likelihood model. Depending on detector design, we observe a 1%-12% improvement of intrinsic resolution for a 1-or-2-hit estimator as compared with a 1-hit estimator. We also observe improved differentiation of photopeak events using a 1-or-2-hit estimator as compared with the 1-hit estimator; more than 6% of photopeak events that were rejected by likelihood filtering for the 1-hit estimator were accurately identified as photopeak events and positioned without loss of resolution by a 1-or-2-hit estimator; for PET, this equates to at least a 12% improvement in coincidence-detection efficiency with likelihood filtering applied.

Modern Field Programmable Gate Arrays (FPGAs) are capable of performing complex discrete signal processing algorithms with clock rates above 100MHz. This combined with FPGA's low expense, ease of use, and selected dedicated hardware make them an ideal technology for a data acquisition system for positron emission tomography (PET) scanners. Our laboratory is producing a high-resolution, small-animal PET scanner that utilizes FPGAs as the core of the front-end electronics. For this next generation scanner, functions that are typically performed in dedicated circuits, or offline, are being migrated to the FPGA. This will not only simplify the electronics, but the features of modern FPGAs can be utilizes to add significant signal processing power to produce higher resolution images. In this paper two such processes, sub-clock rate pulse timing and event localization, will be discussed in detail. We show that timing performed in the FPGA can achieve a resolution that is suitable for small-animal scanners, and will outperform the analog version given a low enough sampling period for the ADC. We will also show that the position of events in the scanner can be determined in real time using a statistical positioning based algorithm.

Modern Field Programmable Gate Arrays (FPGAs) are capable of performing complex discrete signal processing algorithms with clock rates of above 100MHz. This combined with FPGAs low expense, ease of use, and selected dedicated hardware make them an ideal technology for a data acquisition system for positron emission tomography (PET) scanner. Our laboratory is producing a high-resolution, small-animal PET scanner that utilizes FPGAs as the core of the front-end electronics. While this scanner uses an Altera ACEX1k and has limited complexity, we are also developing a new set of front-end electronics based on an Altera StratixII. This next generation scanner utilizes many of the features of modern FPGAs to add significant signal processing to produce higher resolution images. One such process we discuss is sub-clock rate pulse timing. We show that timing performed in the FPGA can achieve a resolution that is suitable for small-animal scanners, and will outperform the analog version given a low enough sampling period for the ADC

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 two-thirds 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 statistical data quality warrants further investigation of the impact of partial collimation on clinical whole-body PET imaging.

Appropriate application of spatially variant system models can correct for degraded resolution response and mispositioning errors. This paper explores the detector blurring component of the system model for a whole body positron emission tomography (PET) system and extends this factor into a more general system response function to account for other system effects including the influence of Fourier rebinning (FORE). We model the system response function as a three-dimensional (3-D) function that blurs in the radial and axial dimension and is spatially variant in radial location. This function is derived from Monte Carlo simulations and incorporates inter-crystal scatter, crystal penetration, and the blurring due to the FORE algorithm. The improved system model is applied in a modified ordered subsets expectation maximization (OSEM) algorithm to reconstruct images from rebinned, fully 3-D PET data. The proposed method effectively removes the spatial variance in the resolution response, as shown in simulations of point sources. Furthermore, simulation and measured studies show the proposed method improves quantitative accuracy with a reduction in tumor bias compared to conventional OSEM on the order of 10%-30% depending on tumor size and smoothing parameter.

We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.