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Because spiral magnetic resonance imaging (MRI) is more robust to motion artifacts than echo planar imaging (EPI), spiral imaging method is more suitable in real-time imaging applications where dynamic processes are to be observed. The major hurdle to use spiral imaging method in real-time applications is its slow reconstruction speed. Since spiral trajectories do not sample data on rectilinear grids, raw data must be regridded before inverse fast Fourier transform (FFT). At present, the computational cost for the spiral reconstruction algorithm is still too high and it is not fast enough to achieve the minimum speed requirement of 20 frames/s for real-time imaging applications. In this paper, we propose to replace floating-point calculations with fixed-point calculations in the reconstruction algorithm to remove the computational bottlenecks. To overcome the quantization and round-off errors introduced by fixed-point calculations, we devise a method to find the optimal precision for the fixed-point representation. Adding with a highly efficient vector-radix two-dimensional (2-D) FFT algorithm and modifications to speed up the gridding convolution, we have cut the reconstruction time by 42% and achieved real-time reconstruction at 30 frames/s for 128 x 128 matrices on low-cost PC's. Index Terms--Fixed-point representations, real-time image reconstruction, spiral magnetic resonance imaging (MRI), two-dimensional (2-D) fast Fourier transform (FFT).