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High-resolution positron emission tomography (PET) scanners have brought many improvements to the nuclear medicine imaging field. However, the mechanical limitations in the construction of the scanners introduced gaps between the detectors, and accordingly, to the acquired projection data. When the methods requiring full-sinogram dataset, e.g., filtered backprojection (FBP) are applied, the missing parts degrade the reconstructed images. In this study, we aim to compensate the sinograms for the missing parts, i.e., gaps. For the gap filling, we propose an iterative discrete-cosine transform (DCT) domain method with two versions: (1) with basic DCT domain filter and (2) with dedicated and gap-dependent DCT domain filter. For the testing of the methods, 2-D FBP reconstructions were applied to the gap-filled sinograms. The proposed DCT domain gap-filling method with two different filters was compared to the constrained Fourier space (CFS) method. For the quantitative analysis, we used numerical phantoms at eight different Poisson noise levels with 100 realizations. Mean-square error, bias, and variance evaluations were performed over the selected regions of interest. Only the dedicated gap-dependent DCT domain filter showed quantitative improvement in all regions, at each noise level. We also assessed the methods visually with a [11C] raclopride human brain study reconstructed by 2-D FBP after gap filling. The visual comparisons of the methods showed that the gap filling with both DCT domain filters performed better than the CFS method. The proposed technique can be used for the sinograms, not only with limited range of projections as in the high-resolution research tomograph (ECAT HRRT) PET scanner, but also with detector failure artifacts.