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Presenter: Shravanthi Musti Venkata
Supervisor:

Date: Tue, March 22, 2022
Time: 15:00:00 - 16:00:00
Place: via Zoom - please see link below

ABSTRACT

Zoom link:  https://uvic.zoom.us/j/88159533517?pwd=KzBaVVhYbGZQWEJEYkpoVzZMUjB2Zz09

Meeting ID: 881 5953 3517

Password: 809239

Note:  Please log in to Zoom via SSO and your UVic Netlink ID.

Abstract: 

Ultrafast plane-wave imaging has become a major medical imaging modality with a tremendous potential to advance ultrasound diagnostics. Plane-wave ultrasound imaging enables data acquisition at very high frame rates with a single insonification but can suffer from degraded image quality. The latter can be improved by using multiple plane-wave pulses emitted at different steering angles, which reduces the frame rate but results in better image quality. With the huge success of deep learning approaches in ultrasound imaging, another promising alternative is to employ an image-enhancing convolutional neural network (CNN) for beamformed data post-processing.

This project explores the use of an efficient CNN to enhance the resolution of ultrasound images. Our objective is to keep the plane-wave (PW) image reconstruction cost low. Hence, this work uses fast Fourier-domain migration (FDM) to process the raw channel data in conjunction with a well-known low-complexity CNN called Efficient Sub-pixel Convolutional Neural Network (ESPCN) to enhance the beamformed data during image reconstruction. To evaluate our approach, we have used two in-vitro experimental datasets from the PICMUS evaluation framework and compared our results with conventional delay-and-sum (DAS) beamforming used for high-resolution image reconstruction.