Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data
- Resource Type
- Conference
- Authors
- Hahne, Christopher; Chabouh, Georges; Couture, Olivier; Sznitman, Raphael
- Source
- 2023 IEEE International Ultrasonics Symposium (IUS) Ultrasonics Symposium (IUS), 2023 IEEE International. :1-4 Sep, 2023
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Signal Processing and Analysis
Radio frequency
Location awareness
Ultrasonic imaging
Transducers
Array signal processing
Microscopy
Superresolution
Super-resolution
Ultrasound
Localization
Deep Learning
Neural Network
Beamforming
- Language
- ISSN
- 1948-5727
Ultrasound Localization Microscopy (ULM) enables imaging of vascular structures in the micrometer range by accumulating contrast agent particle locations over time. Precise and efficient target localization accuracy remains an active research topic in the ULM field to further push the boundaries of this promising medical imaging technology. Existing work incorporates Delay-And-Sum (DAS) beamforming into particle localization pipelines, which ultimately determines the ULM image resolution capability. In this paper we propose to feed unprocessed Radio-Frequency (RF) data into a super-resolution network while bypassing DAS beamforming and its limitations. To facilitate this, we demonstrate label projection and inverse point transformation between B-mode and RF coordinate space as required by our approach. We assess our method against state-of-the-art techniques based on a public dataset featuring in silico and in vivo data. Results from our RF-trained network suggest that excluding DAS beamforming offers a great potential to optimize on the ULM resolution performance.