Abstract
Intravascular ultrasound (IVUS) offers a unique perspective in the treatment of vascular diseases by creating a sequence of ultrasound-slices acquired from within the vessel. However, unlike conventional hand-held ultrasound, the thin catheter only provides room for a small number of physical channels for signal transfer from a transducer-array at the tip. For continued improvement of image quality and frame rate, we present the use of deep reinforcement learning to deal with the current physical information bottleneck. Valuable inspiration has come from the field of magnetic resonance imaging (MRI), where learned acquisition schemes have brought significant acceleration in image acquisition at competing image quality. To efficiently accelerate IVUS imaging, we propose a framework that utilizes deep reinforcement learning for an optimal adaptive acquisition policy on a per-frame basis enabled by actor-critic methods and Gumbel top-K sampling.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1216-1220 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-6654-0540-9 |
DOIs | |
Publication status | Published - 27 Apr 2022 |
Event | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore, Singapore Duration: 23 May 2022 → 27 May 2022 Conference number: 47 https://2022.ieeeicassp.org/ |
Conference
Conference | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 |
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Abbreviated title | ICASSP 2022 |
Country/Territory | Singapore |
City | Singapore |
Period | 23/05/22 → 27/05/22 |
Internet address |
Keywords
- Deep reinforcement learning
- compressed sensing
- intravascular ultrasound