Deep Learning Utilization in Beamforming Enhancement for Medical Ultrasound

Ultrasound imaging offers a low cost, noninvasive and portable system, which allowed it to be an invaluable tool for medical imaging. However, the quality of the reconstructed images depends significantly on the beamforming technique utilized. Although advanced data-adaptive methods of reconstruction such as Minimum Variance (MV) beamforming can recover image quality much higher than conventional techniques, their implementation also entails a heavy computational burden. This dichotomy hinders the ultrasound imaging use as a standalone device in some applications such as early breast cancer detection. Deep neural networks (DNNs) have shown a huge potential when applied to many Artificial intelligence (AI) research fields. In this work, the use of Deep learning in improving the quality of the beamforming technique Delay and Sum (DAS) normally used for ultrasound (US) images reconstruction is explored. Three different architectures are implemented: Convolutional AutoEncoder (CAE), Fully Connected network (FC) and U-Net-like architecture. They were trained on datasets simulated using field II. The dataset consists of input-output pairs where the input is Noisy DAS beamformed scan lines and the output is MV beamformed non-noisy scan lines. The networks show a great ability in predicting the beamformed signals along with significantly reducing noise in the reconstructed images. Additionally, the proposed networks improve other image characteristics such as scatterer size and position along with reducing tail characteristic normally found in DAS beamformed ultrasound images. US images constructed by the networks achieved better quality metrics that surpass conventional DAS beamformed images. The CAE, U-net-like architecture, and FC enhanced the signal to noise ratio (SNR) compared to DAS by 218%, 165% and 136% respectively. Additionally, the networks showed higher Contrast to Noise Ratio (CNR) and Contrast Ratio (CR) metrics than DAS beamformed signals. Finally, the proposed approach achieves a 60% enhancement in time consumption of image reconstruction compared to MV technique, which allows higher possible frame rate with a comparable outcome.