The aim of this study was to develop a geometric calibration method capable of eliminating the reconstruction artifacts of geometric misalignments in a tomosynthesis prototype with dual-axis scanning geometry. The potential scenarios of geometric misalignments were demonstrated, and their effects on reconstructed images were also evaluated. This method was a phantom-based approach with iterative optimization, and the calibration phantom was designed for specific tomosynthesis scanning geometry. The phantom was used to calculate a set of geometric parameters from each projection, and these parameters were then used to evaluate the geometric misalignments of the dual-axis scanning-geometry prototype. The simulated results revealed that the extracted geometric parameters were similar to the input values and that the artifacts of reconstructed images were minimized due to geometric calibration. Additionally, experimental chest phantom imaging results also indicated that the artifacts of the reconstructed images were suppressed and that object structures were preserved through calibration. And the quantitative analysis result also indicated that the MTF can be further improved with the geometric calibration. All the simulated and experimental results demonstrated that this method is effective for tomosynthesis with dual-axis scanning geometry. Furthermore, this geometric calibration method can also be applied to other tomography imaging systems to reduce geometric misalignments and be used for different geometric calibration phantom configurations.
Micro positron emission tomography (PET) and micro single-photon emission computed tomography (SPECT), used for imaging small animals, have become essential tools in developing new pharmaceuticals and can be used, among other things, to test new therapeutic approaches in animal models of human disease, as well as to image gene expression. These imaging techniques can be used noninvasively in both detection and quantification. However, functional images provide little information on the structure of tissues and organs, which makes the localization of lesions difficult. Image fusion techniques can be exploited to map the functional images to structural images, such as X-ray computed tomography (CT), to support target identification and to facilitate the interpretation of PET or SPECT studies. Furthermore, the mapping of two functional images of SPECT and PET on a structural CT image can be beneficial for those in vivo studies that require two biological processes to be monitored simultaneously. This paper proposes an automated method for registering PET, CT, and SPECT images for small animals. A calibration phantom and a holder were used to determine the relationship among three-dimensional fields of view of various modalities. The holder was arranged in fixed positions on the couches of the scanners, and the spatial transformation matrix between the modalities was held unchanged. As long as objects were scanned together with the holder, the predetermined matrix could register the acquired tomograms from different modalities, independently of the imaged objects. In this work, the PET scan was performed by Concorde's microPET R4 scanner, and the SPECT and CT data were obtained using the Gamma Medica's X-SPECT/CT system. Fusion studies on phantoms and animals have been successfully performed using this method. For microPET-CT fusion, the maximum registration errors were 0.21 mm +/- 0.14 mm, 0.26 mm +/- 0.14 mm, and 0.45 mm +/- 0.34 mm in the X (right-left), Y (upper lower), and Z (rostral-caudal) directions, respectively; for the microPET-SPECT fusion, they were 0.24 mm +/- 0.14 mm, 0.28 mm +/- 0.15 mm, and 0.54 mm +/- 0.35 mm in the X, Y, and Z directions, respectively. The results indicate that this simple method can be used in routine fusion studies.
GEANT4 Application for Tomographic Emission (GATE) is a powerful Monte Carlo simulator that combines the advantages of the general-purpose GEANT4 simulation code and the specific software tool implementations dedicated to emission tomography. However, the detailed physical modelling of GEANT4 is highly computationally demanding, especially when tracking particles through voxelized phantoms. To circumvent the relatively slow simulation of voxelized phantoms in GATE, another efficient Monte Carlo code can be used to simulate photon interactions and transport inside a voxelized phantom. The simulation system for emission tomography (SimSET), a dedicated Monte Carlo code for PET/SPECT systems, is well-known for its efficiency in simulation of voxel-based objects. An efficient Monte Carlo workflow integrating GATE and SimSET for simulating pinhole SPECT has been proposed to improve voxelized phantom simulation. Although the workflow achieves a desirable increase in speed, it sacrifices the ability to simulate decaying radioactive sources such as non-pure positron emitters or multiple emission isotopes with complex decay schemes and lacks the modelling of time-dependent processes due to the inherent limitations of the SimSET photon history generator (PHG). Moreover, a large volume of disk storage is needed to store the huge temporal photon history file produced by SimSET that must be transported to GATE. In this work, we developed a multiple photon emission history generator (MPHG) based on SimSET/PHG to support a majority of the medically important positron emitters. We incorporated the new generator codes inside GATE to improve the simulation efficiency of voxelized phantoms in GATE, while eliminating the need for the temporal photon history file. The validation of this new code based on a MicroPET R4 system was conducted for (124)I and (18)F with mouse-like and rat-like phantoms. Comparison of GATE/MPHG with GATE/GEANT4 indicated there is a slight difference in energy spectra for energy below 50 keV due to the lack of x-ray simulation from (124)I decay in the new code. The spatial resolution, scatter fraction and count rate performance are in good agreement between the two codes. For the case studies of (18)F-NaF ((124)I-IAZG) using MOBY phantom with 1 × 1 × 1 mm(3) voxel sizes, the results show that GATE/MPHG can achieve acceleration factors of approximately 3.1 × (4.5 ×), 6.5 × (10.7 ×) and 9.5 × (31.0 ×) compared with GATE using the regular navigation method, the compressed voxel method and the parameterized tracking technique, respectively. In conclusion, the implementation of MPHG in GATE allows for improved efficiency of voxelized phantom simulations and is suitable for studying clinical and preclinical imaging.
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