@inproceedings{558ceedd5c054272b25a47b006f7329a,
title = "Memory-efficient neural network for non-linear ultrasound computed tomography reconstruction",
abstract = "Deep neural networks have proven to excel classical medical image reconstruction techniques. Some networks are based on fully connected (FC) layers to achieve domain transformation such as from the data acquisition domain to the image domain. However, FC layers result in huge numbers of parameters which take a lot of GPU memory. Hence, they do not scale well, and the overall performance is limited. For ultrasound computed tomography (USCT) application, we propose a memory-efficient convolutional network that reconstructs images from the frequency domain to image domain with much less parameters compared with multilayer perceptron, by using data-driven learning. Extensive experiments demonstrate that our method achieves high reconstruction quality. It improves the structural similarity measure (SSIM) from 0.73 to 0.99 when compared with state-of-the-art reconstruction methods in this field while reduces 2/3 parameters when compared with deep neural network with FC layers to reconstruct images from frequency domain to image domain. ",
keywords = "Deep learning, Image reconstruction, Memory efficiency, Ultrasound computed tomography",
author = "Yuling Fan and Hongjian Wang and Hartmut Gemmeke and Torsten Hopp and Dongen, {Koen Van} and Jurgen Hesser",
year = "2021",
doi = "10.1109/ISBI48211.2021.9434164",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE",
pages = "429--432",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
address = "United States",
note = "18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
}