Unsupervised Deep Learning For Accelerated High Quality Echocardiography.

Echocardiography is a pivotal imaging tool for emergency medicine. Unfortunately, it suffers from poor image quality due to the intrinsic limitations of sonography systems. Towards this end, a better quality can be achieved at the cost of reduced frame rate by increasing the number of transmit/receive events and utilizing computationally expensive noise suppression algorithms. However, this visual quality and temporal resolution trade-off is a bottleneck for many echocardiography applications. Conventional acceleration methods, such as multi-line acquisition (MLA), work only for limited acceleration factors and produce blocking artifacts at a high frame rate. Accordingly, various machine learning algorithms have been designed to reduce blocking artifacts in MLA. These algorithms require access to either high-quality raw RF data or time-delayed baseband IQ data. Unfortunately, in many lower-end commercial systems, such data are not accessible. On the other hand, ultrasound images are badly affected by speckle noises which significantly reduces the image quality. We propose an image domain unsupervised deep learning framework using cycleGAN architecture for high quality accelerated echocardiography that simultaneously reduces the blocking artifacts and the speckle noise. The method is evaluated on real in-vivo and phantom data and achieves notable performance gain.