High frame-rate cardiac ultrasound imaging with deep learning
This work addresses image quality issues in cardiac ultrasound for medical imaging applications, representing an incremental improvement over existing methods.
The paper tackled the problem of block artifacts in high frame-rate cardiac ultrasound imaging using multi-line acquisition (MLA) by proposing a deep learning approach that trains a convolutional neural network on real ultrasound data pairs. The result was a significant improvement in image quality for 5- and 7-line MLA, achieving a decorrelation measure similar to single-line acquisition while maintaining MLA's frame rate.
Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.