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Prostate Magnetic Resonance Imaging (MRI) and ultrasound (US) imaging play a crucial role in the diagnosis and management of prostate diseases. However, spatial and axial resolution limitations can hinder the accurate detection of lesions, affecting clinical decision-making. Traditional deep learning-based super-resolution (SR) methods, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have demonstrated success in enhancing medical image quality; however, they often suffer from high computational costs and rigid grid-based representations. In this work, we explore the application of Implicit Neural Representations (INRs), specifically Sinusoidal Representation Networks (SIREN), for super-resolution reconstruction of prostate MRI and US images. While INRs leverage continuous function representations to enhance spatial and axial resolution and preserve fine anatomical structures, our work focuses on the novel application of SIREN to this specific medical imaging task. To further improve reconstruction quality, we propose a hybrid loss function combining Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM). Experimental results demonstrate that our approach effectively restores high-resolution details, improving lesion visibility and aiding radiologists in more accurate diagnosis.Clinical Relevance- Limitations in the spatial and axial resolution of prostate MRI and US can hinder accurate lesion detection, leading to diagnostic uncertainty and the need for additional imaging studies or biopsies. This increases healthcare costs and patient burden. The proposed super-resolution approach using Implicit Neural Representations (INRs) enhances image quality while preserving fine anatomical structures, enabling radiologists to extract more information from existing scans. By improving lesion visibility and diagnostic accuracy, this method has the potential to reduce the need for additional procedures, ultimately leading to cost savings and improved patient outcomes.