Low Complexity 3D Ultrasound Imaging Using Synthetic Aperture Sequential Beamforming
| Autor: | Jian Zhou, J. Brian Fowlkes, Chaitali Chakrabarti, Rungroj Jintamethasawat, Richard Sampson, Oliver D. Kripfgans, Ming Yang, Siyuan Wei, Thomas F. Wenisch |
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| Rok vydání: | 2016 |
| Předmět: |
Hardware architecture
Synthetic aperture radar Beamforming medicine.diagnostic_test Computer science business.industry Aperture Image quality Ultrasound Process (computing) 020206 networking & telecommunications 02 engineering and technology 01 natural sciences 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine 3D ultrasound Computer vision Artificial intelligence business 010301 acoustics Algorithm |
| Zdroj: | SiPS |
| DOI: | 10.1109/sips.2016.14 |
| Popis: | Synthetic aperture sequential beamforming (SASB) is a technique to achieve range-independent resolution in 2D images with lower computational complexity compared to synthetic aperture ultrasound (SAU). It is a two stage process, wherein the first stage performs fixed-focus beamforming followed by dynamic-focus beamforming in the second stage. In this work, we extend SASB to 3D imaging and propose two schemes to reduce its complexity:(1) reducing the number of elements in both transmit and receive and (2) implementing separable beamforming in the second stage. Our Field-II simulations demonstrate that reducing transmit and receive apertures to 32×32 and 16×16 elements, respectively, and using separable beamforming reduces 3D SASB computational complexity by 15× compared to the 64×64 aperture case with almost no loss in image quality. We also describe a hardware architecture for 3D SASB that performs first-stage beamforming in the scan head, reducing the amount of data that must be transferred for offchip processing in the second stage beamformer by up to 256×. We describe an implementation approach for the second stage that performs an optimized in-place update for both steps of separable beamforming and is well suited for GPU. |
| Databáze: | OpenAIRE |
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