In recent years, high resolution adaptive minimum variance-based beamformers have been successfully applied to medical ultrasound imaging to improve its resolution and contrast, simultaneously. However, these improvements come at the cost of much more computational complexity in comparison to the non-adaptive delay-and-sum beamformer. The computational overhead mainly results from the Symbol covariance matrix inversion needed for computation of the adaptive weights, the complexity of which is cubic with the subarray size, Symbol. In medical ultrasound imaging with focusing on the imaging point, we have a limited number of dominant modes and there is no need for the full matrix inversion. Based on this idea, we have investigated the application of the dominant mode rejection (DMR) adaptive beamformer for medical ultrasound imaging, which uses only some largest dominant modes to approximate the covariance matrix in dominant subspace. We show, using simulated and experimental data, that this subspace dimension can be selected as low as two resulting in significant computational complexity reduction while still achieving performance comparable to that of the minimum variance beamformer.