The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
So you think you can DAS? A viewpoint on delay-and-sum beamforming
Open accessAbstract
Delay-and-sum (DAS) is the most widespread digital beamformer in high-frame-rate ultrasound imaging. Its implementation is simple and compatible with real-time applications. In this viewpoint article, we describe the fundamentals of DAS beamforming. The underlying theory and numerical approach are detailed so that users can be aware of its functioning and limitations. In particular, we discuss the importance of the f-number and speed of sound on image quality, and propose one solution to set their values from a physical viewpoint. We suggest determining the f-number from the directivity of the transducer elements and the speed of sound from the phase dispersion of the delayed signals. Simplified Matlab codes are provided for the sake of clarity and openness. The effect of the f-number and speed of sound on the lateral resolution and contrast-to-noise ratio was investigated in vitro and in vivo. If not properly preset, these two factors had a substantial negative impact on standard metrics of image quality (namely CNR and FWHM). When beamforming with DAS in vitro or in vivo, it is recommended to optimize these parameters in order to use it wisely and prevent image degradation.
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Cites
Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography
Montaldo, G., Tanter, M., Bercoff, J., Benech, N., & Fink, M. (2009) · IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2009.1067 · Cited by 1,814
Diagnostic Ultrasound Imaging: Inside Out
(2014) · Elsevier eBooks · doi:10.1016/c2011-0-07261-7 · Cited by 1,093
Diagnostic ultrasound Imaging : Inside out
(2005) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/s-2005-861725 · Cited by 761
Adaptive Beamforming Applied to Medical Ultrasound Imaging
Synnevag, J. F., Austeng, A., & Holm, S. (2007) · IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2007.431 · Cited by 592
Sparse Matrices in MATLAB: Design and Implementation
Gilbert, J. R., Moler, C., & Schreiber, R. (1992) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0613024 · Cited by 591
Camacho, J., Parrilla, M., & Fritsch, C. (2009) · IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2009.1128 · Cited by 525
Banerjee, M., Cui, X., Li, Z., Yu, H., Cai, L., Jia, X., He, D., Wang, C., Gao, T., & Xie, Z. (2018) · Scientific Reports · doi:10.1038/s41598-018-29995-2 · Cited by 506
Destrempes, F., & Cloutier, G. (2010) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2010.04.001 · Cited by 196
Principles of Digital Image Processing: Core Algorithms
Burger, W., & Burge, M. J. (2009) · Undergraduate Topics in Computer Science · doi:10.1007/978-1-84800-195-4 · Cited by 191
Deep Neural Networks for Ultrasound Beamforming
Luchies, A. C., & Byram, B. C. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2809641 · Cited by 182
Cited by
A survey on deep learning in medical ultrasound imaging
Song, K., Feng, J., & Chen, D. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1398393 · Cited by 59
Metadata
- Authors
- Perrot, V., Polichetti, M., Varray, F., & Garcia, D.
- Venue
- Ultrasonics
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 111
- Pages
- 106309
- ISSN
- 0041-624X (ISSN-L)
- OpenAlex
- W3044863663
- Cited by
- 377
- References
- 30
- FWCI
- 15.69
- Type
- journal article
- Language
- en
- Open access
- bronze
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