Simulated data has always been a valuable tool for the validation of fMRI data, models, and experimental designs. Mitigating the expensive time and monetary costs associated with fMRI data acquisition. In fMRI, spatial frequency measurements are obtained as complex-valued (CV) data points which has been dubbed k-space. Composed of two parts, characterized as real and imaginary or magnitude and phase, both are utilized in the reconstruction process to form a time series of of images. However, it has become common practice to not utilize any phase information in the statistical analysis of the reconstructed images, essentially discarding half of the collected data. Current simulations tools available tend to uphold this practice by producing magnitude only image space datasets without regard for the CV reconstruction process and the importance of phase information. A CV k-space data set generated from the nuclear magnetic resonance (NMR) signal equation would take into consideration the timing parameters, noise parameters, and hardware constraints. Incorporating easily obtainable mapping information such as T1, T2, proton spin density, and others would best reflect experimental data.