Prostate cancer is one of the most commonly diagnosed cancers worldwide, yet working towards more accurate and cost-effective detection strategies for this disease remains an active area of research. This includes introducing a new method called micro-ultrasound (microUS), which has equal performance to the gold standard multiparametric magnetic resonance imaging for prostate biopsy guidance. Cancerous lesions are often stiffer than their healthy surroundings, and this tissue stiffness can be imaged using elastography. Clinical strain elastography generally uses manual compression of the tissue via the probe face; however, this method is highly user-dependent and can result in unreliable images due to the complexity and steep learning curve to apply ideal compression. Here, we implement and validate a relative elastography method called transfer function (TF) imaging, which uses automatic tissue compression from a voice coil motor attached to the microUS probe for excitation, and calculates the tissue’s relative stiffness from its frequency response to this excitation. We demonstrate our method’s improved repeatability compared to manual strain elastography using quantitative and qualitative evaluations performed using a commercial quality assurance elasticity phantom. Overall, this method makes elastography much simpler for clinicians, further enabling its use in guiding prostate biopsy procedures.