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  • Title: Gap-filling methods for 3D PlanTIS data.
    Author: Loukiala A, Tuna U, Beer S, Jahnke S, Ruotsalainen U.
    Journal: Phys Med Biol; 2010 Oct 21; 55(20):6125-39. PubMed ID: 20871138.
    Abstract:
    The range of positron emitters and their labeled compounds have led to high-resolution PET scanners becoming widely used, not only in clinical and pre-clinical studies but also in plant studies. A high-resolution PET scanner, plant tomographic imaging system (PlanTIS), was designed to study metabolic and physiological functions of plants noninvasively. The gantry of the PlanTIS scanner has detector-free regions. Even when the gantry of the PlanTIS is rotated during the scan, these regions result in missing sinogram bins in the acquired data. Missing data need to be estimated prior to the analytical image reconstructions in order to avoid artifacts in the final reconstructed images. In this study, we propose three gap-filling methods for estimation of the unique gaps existing in the 3D PlanTIS sinogram data. The 3D sinogram data were gap-filled either by linear interpolation in the transaxial planes or by the bicubic interpolation method (proposed for the ECAT high-resolution research tomograph) in the transradial planes or by the inpainting method in the transangular planes. Each gap-filling method independently compensates for slices in one of three orthogonal sinogram planes (transaxial, transradial and transangular planes). A 3D numerical Shepp-Logan phantom and the NEMA image quality phantom were used to evaluate the methods. The gap-filled sinograms were reconstructed using the analytical 3D reprojection (3DRP) method. The NEMA phantom sinograms were also reconstructed by the iterative reconstruction method, ordered subsets maximum a posteriori one step late (OSMAPOSL), to compare the results of gap filling followed by 3DRP with the results of OSMAPOSL reconstruction without gap filling. The three methods were evaluated quantitatively (by mean square error and coefficients of variation) over the selected regions of the 3D numerical Shepp-Logan phantom at eight different Poisson noise levels. Moreover, the NEMA phantom scan data were used in visual assessments of the methods. We observed that all methods improved the reconstructed images both quantitatively and visually. Therefore, the proposed gap-filling methods followed by the analytical 3DRP are alternative for the reconstructions of not only the 3D PlanTIS data, but also other PET scanner data of the ClearPET family.
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