High-intensity focused ultrasound (HIFU) is a minimally invasive medical procedure that uses ultrasonic waves to ablate or heat tissue with the aim of treating tumors and tremors. Diagnostic ultrasound imaging is the primary mode of imaging during HIFU treatments due to its real-time capabilities. However, HIFU noise, produced from therapeutic ultrasound components, interfere with the diagnostic ultrasound components and cause difficulty in monitoring changes to tissue during treatment. In a multitude of HIFU treatments, deep learning has been used as a tool to detect coagulation, monitor temperature, and segment tumors. Convolutional neural network (CNN) models are a series of deep learning algorithms that can assign importance to aspects of an inputted image and differentiate one from the other. Based on previous methods of filtering, CNNs too can be trained to filter raw RF signals received by an ultrasound probe for subsequent real-time treatment feedback with HIFU. Here, we were able to present a preliminary investigation of a CNN approach for HIFU noise reduction. To do this, we used acoustic wave simulations from k-Wave, a time-domain, full-wave model for ultrasound wave propagation, in combination with the Deep Learning Toolbox from MATLAB. Subsequent analyses studied the performance of noise reduction via the proposed regression model.