High quality ultrasonic multi-line transmission through deep learning
This work addresses image quality degradation in ultrasound imaging for medical applications, representing an incremental improvement over existing MLT methods.
The paper tackled the problem of cross-talk artifacts in multi-line transmission (MLT) for cardiac ultrasound imaging, which reduces acquisition time but degrades image quality, by introducing a deep learning-based method that significantly improves visual quality and objective metrics like contrast ratio and contrast-to-noise ratio while preserving resolution.
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography. Several methods have been proposed in the medical ultrasound literature aiming at accelerating the image acquisition. In this paper, we consider one such method called \textit{multi-line transmission} (MLT), in which several evenly separated focused beams are transmitted simultaneously. While MLT reduces the acquisition time, it comes at the expense of a heavy loss of contrast due to the interactions between the beams (cross-talk artifact). In this paper, we introduce a data-driven method to reduce the artifacts arising in MLT. To this end, we propose to train an end-to-end convolutional neural network consisting of correction layers followed by a constant apodization layer. The network is trained on pairs of raw data obtained through MLT and the corresponding \textit{single-line transmission} (SLT) data. Experimental evaluation demonstrates significant improvement both in the visual image quality and in objective measures such as contrast ratio and contrast-to-noise ratio, while preserving resolution unlike traditional apodization-based methods. We show that the proposed method is able to generalize well across different patients and anatomies on real and phantom data.