Due to long acquisition times, the use of magnetic resonance imaging (MRI) remains challenging in some applications. Variable-density acquisitions enable scan acceleration while maintaining a desirable trade-off between signal-to-noise ratio (SNR) and spatial resolution. Several image-domain and k-space algorithms were previously proposed for parallel-imaging reconstructions of variabledensity acquisitions. However, these methods involve iterative procedures for non-Cartesian data, resulting in substantial computational burden in particular for three-dimensional (3D) reconstructions. An efficient method based on partially parallel imaging with localized sensitivities (PILS) was recently proposed for fast reconstructions of 2D non-Cartesian data. This thesis introduces a generalized image-domain implementation for 3D non-Cartesian variable-density data, and compares it against conventional gridding, PILS, and ESPIRiT (iterative self-consistent parallel imaging reconstruction using eigenvector maps) reconstructions on brain and knee data accelerated at R=2.5 to 4.2. The results indicate that the proposed 3D variable-FOV method outperforms SOS (sum of squares) and PILS methods, and performs equally or better than ESPIRiT reconstruction at less than half of the processing time required by ESPIRiT. Thus, the proposed method provides fast, high-SNR, artifact-suppressed reconstructions.