Positron emission tomography (PET) is an important imaging modality in both clinical usage and research
studies. For small-animal PET imaging, it is of major interest to improve the sensitivity and resolution. We
have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector
heads. The highly accurate system response matrix can be computed by use of Monte Carlo simulations, and
stored for iterative reconstruction methods. The detector head employs 2.1x2.1x20 mm3 LSO/LYSO crystals of
pitch size equal to 2.4 mm, and thus will produce more than 224 millions lines of response (LORs). By exploiting
the symmetry property in the dual-head system, the computational demands can be dramatically reduced.
Nevertheless, the tremendously large system size and repetitive reading of system response matrix from the hard
drive will result in extremely long reconstruction times. The implementation of an ordered subset expectation
maximization (OSEM) algorithm on a CPU system (four Athlon x64 2.0 GHz PCs) took about 2 days for 1
iteration. Consequently, it is imperative to significantly accelerate the reconstruction process to make it more
useful for practical applications. Specifically, the graphic processing unit (GPU), which possesses highly parallel
computational architecture of computing units can be exploited to achieve a substantial speedup. In this work, we
employed the state-of-art GPU, NVIDIA Tesla C2050 based on the Fermi-generation of the compute united device
architecture (CUDA) architecture, to yield a reconstruction process within a few minutes. We demonstrated
that reconstruction times can be drastically reduced by using the GPU. The OSEM reconstruction algorithms
were implemented employing both GPU-based and CPU-based codes, and their computational performance was
quantitatively analyzed and compared.
We believe that small-animal positron emission tomography (μPET) can play an important role in phenotyping and drug screening. For such applications, imaging throughput becomes an important issue because one needs to image a
considerable number of subjects in a study. Toward enabling high-throughput μPET imaging, we are developing
a prototype that consists of two large-area, high-performance flat detectors. These detectors are placed opposed to each other with a small spacing for
providing large detection solid angle and detection sensitive volume. The resulting scanner geometry produces data having missing views and projection truncations, therefore posing a particular challenge in
reconstruction. In this paper, we developed a new iterative reconstruction method that addresses this challenge. By using 2D simulated data, we find that this new method can accurately reconstruct an extended
detection volume of the prototype. Because our prototype shares the same configuration with positron emission mammography (PEM), the new reconstruction method is also applicable for PEM reconstruction.
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