Abstract
Sparse and low-rank decomposition, also known as robust principle component analysis, has been applied successfully in numerous applications. Typically, this approach leads to a minimization problem which is solved using iterative algorithms. Drawing inspiration from recurrent networks, in recent years deep-learning strategies have been extended to mimic the behavior of iterative algorithms, with reduced complexity. In this work, we propose an extension of these deep architectures to robust principle component analysis in which fully-connected layers are replaced with convolutional ones. This strategy offers spatial invariance and significant reduction in the number of learned parameters. We then apply the proposed method to contrast-enhanced ultrasound, in which low-rank tissue signal needs to be removed in order to visualize blood vessels. We demonstrate the effectiveness of our approach on simulations and in-vivo rat brain scans. The resulting images exhibit improved visual quality and contrast compared with images obtained by commonly practiced methods.
Original language | English |
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Title of host publication | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 3212-3216 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4799-8131-1 |
DOIs | |
Publication status | Published - May 2019 |
Event | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) - Brighton Conference Centre, Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 https://2019.ieeeicassp.org/ |
Conference
Conference | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) |
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Abbreviated title | ICASSP 2019 |
Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/19 → 17/05/19 |
Internet address |
Keywords
- Contrast agents
- Deep learning
- Low-rank decomposition
- Ultrasound
- Unfolding