Hyperechogenicity of the substantia nigra (SN) in the 'butterfly shaped' midbrain is a widely recognized diagnostic marker to differentiate between the early stages of Parkinsons Disease (PD) and other diseases which cause parkinsonian symptoms. While clinical differentiation of these diseases can be difficult, hyperechogenicity of the SN is only common in PD patients. Transcranial B-mode Ultrasound Imaging (TCUI) has become a heavily relied upon method to detect echogenicity in the brain. While standard B-mode imaging can show the presence of SN hyperechogenicity, it may not be able to do so with high enough specificity for reliably accurate diagnoses. The cutoff of what is considered a normal echogenic size is 0.2cm2. Clearly, boundary definition is of the utmost importance to avoid overestimating the size of the echogenic area. Many studies have shown that the harmonic component of ultrasound images have better dynamic range than standard B-mode images. That is, the images show greater contrast between light and dark regions, so low energy noise signals are less likely to show up in the image. Whereas a simple bandpass filter across the harmonic frequency would contain interference from the noisy fundamental component due to overlap of the frequency bands. We propose the use of an adaptive Third Order Volterra Filter (TOVF), which is a nonlinear filter that separates a B-mode image into its linear, quadratic, and cubic components regardless of spectral overlap. This paper investigates several variants of the commonly used adaptive Least Mean Squared (LMS) algorithm for determining filter coefficients, and their potential to improve dynamic range and resolution in B-mode images compared to a standard LMS algorithm. We found that several variant algorithms indeed show improvement in terms of Power Spectral Density (PSD) at the harmonics.