Ultrasound imaging is a safe and popular imaging technique that relies on received radio frequency (RF) echos to show the internal organs and tissue. B-mode (Brightness mode) is the typical mode of ultrasound images generated from RF signals. In practice, the real processing algorithms from RF signals to B-mode images in ultrasound machines are kept confidential by the manufacturers. The thesis aims to estimate the process and reproduce the same results as the Ultrasonix One ultrasound machine does using deep learning. 11 scalar parameters including global gain, time-gain-compensation (TGC1-8), dynamic range and reject affect the transformation from RF signals to B-mode images in the machine. Data generation strategy was proposed. Two network architectures adapted from U-Net and Tiramisu Net were investigated and compared. Results show that a deep learning network is able to translate RF signals to B-mode images with respect to the controlling parameters. The best performance is achieved by adapted U-Net that reduces per pixel error to 1.325%. The trained model can be used to generate images for other experiments.