Magnetic particle imaging (MPI) is a high-contrast imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles by exploiting their nonlinear response. In MPI, image reconstruction is performed via two di erent methods: system function reconstruction (SFR) and x-space reconstruction. For the SFR approach, analysis of various scanning trajectories provided important insight about their image quality performances. While Cartesian trajectories remain the most popular choice for x-space-based reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove bene cial for improving image quality. In this thesis, a generalized reconstruction scheme is proposed for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, ve di erent trajectories were utilized with varying density levels. Comparison to alternative reconstruction methods show signi cant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The fully automated gridding reconstruction proposed in this thesis can be utilized with these trajectories to improve the image quality in x-space MPI.