Compensation of missing projection data for MDAPET camera

The MDAPET camera is a prototype multiring PET scanner with variable transaxial field of view (FOV) that allows for the resizing of the patient opening for optimizing the detection sensitivity for different body cross-sections. This variable FOV feature introduces gaps between the modules, which cause gaps in the sinograms. To fill the gaps, the camera is rotated during data acquisition. We have studied the possibility of not rotating the gantry in the brain mode, where the gaps are smallest, and estimating the missing data. For three-dimensional (3D) data, three methods of compensation were evaluated. Without any compensation, artifacts dominated the images. In the first method, the missing data were estimated using linear interpolation in the angular direction of the sinogram. In the second, the missing data for each pixel was estimated using the average value of several pixels in the angular direction. In the third method, the missing data were first estimated using the averaging or linear interpolation method to construct preliminary images. These images were then used to create new projections and by appropriate weighting these new data were used to fill the gaps in the original data. We also evaluated the effect of rebinning the 3D sinograms into two-dimensional (2D) sinograms and then performing a 2D image reconstruction. All the methods produced almost artifact-free images, but none were able to match the quality of the images reconstructed from the 3D data that were acquired with gantry rotation.