Deep Learning in Signal Linearization for Harmonic Imaging Application

Harmonic imaging's popularity arises from its ability to produce high contrast resolution images. However, its need for at least two successive firings remains a hindering factor for a faster imaging process. In this work, a novel approach for ultrasound tissue harmonic imaging using a single firing is introduced utilizing deep learning concepts. This is achieved by implementing a network to predict the linear signal component output from a received nonlinear echo signal as input. Two different architectures were implemented: Convolutional AutoEncoder (CAE) and U-Net - like architecture. The dataset consists of 6k 3D focused K-wave simulations of multi scatterers varying in position, radius and speed of sound in a tissue-like medium with speckle noise. Each simulation is performed twice with the same tissue properties in a linear and a nonlinear environment. For each transmission, a transmission frequency of 7.5MHz was used and the acquired raw RF signals were sampled. The networks achieved a Mean Squared Error (MSE) value of 9.1x10(-06), on the validation set between the linear ground truth signals and the predicted output. Moreover, the Total Harmonic Distortion (THD) value in the model's predicted results is 1.615% compared to 31.75% in the nonlinear environment demonstrating an enhancement in harmonics suppression by 91.3%. Furthermore, the proposed technique is exploited in harmonic imaging by subtracting the predicted linear component from the received nonlinear echo to suppress the fundamental frequency. This harmonic imaging approach achieved an average THD of 119.5%, while the conventional Pulse Amplitude Modulation (PAM) method achieved 71.22% allowing a better harmonic to fundamental ratio. These results open the door for the implementation of harmonic imaging with a comparable quality to the conventional PAM technique, yet with an increased frame-rate and reduced motion artifacts.