During the early stage of the disease, idiopathic Parkinson's Disease can be very difficult to differentiate from atypical parkinsonian syndromes. Hyperechogenicity in the substantia nigra is one marker that has been shown to help make this differential diagnosis, and Transcranial Ultrasound Imaging is the preferred method for detecting SN hyperechogenicity. Hyperechogenicity is defined as an echogenic area larger than 0.2cm². However, B-mode imaging often contains enough noise that the boundary may not be clear, thus making this diagnosis much more difficult. Harmonic imaging using a Third- Order Volterra filter is one solution that has been shown to be successful in filtering out the noise in these images. In this paper we show that regularization methods such as the Truncated Singular Value Decomposi- tion and Damped Singular Value Decomposition can be used to solve for the Volterra Filter's coefficients much more quickly than adaptive Least Mean Squared methods without sacrifice in image quality. These findings have significant implications for the viability of using the Volterra Filter in real-time applications.