KEYWORDS: Spherical lenses, Near field, Point spread functions, Voxels, Monte Carlo methods, Coded apertures, Cameras, Source localization, Device simulation, Collimators
Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Unlike other types of collimators, a coded aperture offers the possibility of 3D imaging of small spherical sources in the near field. However, due to the non-linear system model this option has rarely been investigated. We hypothesize that a deep learning approach is capable of 3D localization of spherical sources with an accuracy that is required for sentinel lymph node biopsy. In order to generate a sufficient amount of training data and densely sample the field of view Monte Carlo simulations are computationally too expensive. Thus, a fast and accurate simulation framework called ConvSim is presented. It computes the detector image from a 3D volume distribution of gamma sources in front of the gamma camera described as a voxel cube. By slice-wise convolutions with the corresponding point spread functions (PSF) a simulation time of few seconds is reached. For spherical sources non-linear near field effects are considered additionally. Comparing the generated detector images of exemplary spherical sources at different distances to Monte Carlo simulations yield a good correspondence with a multi-scale structural similarity index (MS-SSIM) between 0.65 and 0.91. Even though ConvSim entirely ignores the photon energy, the simulation framework serves as a fast and accurate alternative to time-consuming Monte Carlo simulations. For the future, the framework might be useful for investigating new types of coded apertures or the reconstruction of extended sources. ConvSim is available at https://github.com/tomeiss/convsim.
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