Abstract
High-frame-rate volumetric ultrasound imaging is highly desired to enable novel clinical ultrasound applications. However, realizing high-quality volumetric ultrasound imaging at a high frame rates (>500 Hz) is challenging. Keeping the cable count and data rate of the transducer device at a realistic level without sacrificing image quality to an undesirable extend means that a dedicated design with carefully chosen trade-offs is required and powerful processing of the received signals is desired. This thesis describes the development of a high-frame-rate 3D ultrasound transducer through dedicated transducer design and explores the use of deep learning-based beamforming to achieve high-quality 3D imaging. Specifically, the first part of this thesis focuses on the development of an imaging scheme and the realization and testing of two prototype transducers for high-frame-rate 3D intracardiac echography (3D-ICE). The second part of the thesis implements deep learning in the image reconstruction process to improve the image quality of volumetric ultrasound. Deep learning-based beamforming is implemented and evaluated first for a miniature matrix array, which similar to the 3D-ICE design applies micro-beamforming to achieve cable count reduction and finally for a spiral array which uses a sparse distribution of transducer channels.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Thesis sponsors | |
Award date | 28 Jun 2023 |
Print ISBNs | 978-94-6366-701-2 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- ultrasound
- 3D
- ICE
- high frame rate
- matrix transducer array
- deep learning
- beamforming