aceAdvancing Ultrasound Beamforming With Deep Learning: A Comprehensive Review of Methods, Datasets, Benchmarks, and Computational Challenges
Ultrasound imaging is a widely used diagnostic tool, and beamforming techniques are integral to its performance. Traditional methods such as delay-and-sum (DAS) and delay-multiply-and sum (DMAS) have limitations in terms of image quality and computational efficiency. Recently, deep learning approaches have shown significant promise in improving ultrasound beamforming, offering enhanced image resolution and quality, alongside potential for real-time processing. This review is aimed at providing a comprehensive ov…
Deep learning models for ultrasound beamforming can improve image resolution and quality over traditional DAS methods and enable real-time processing when paired with FPGA acceleration.
Deep learning beamforming models are constrained by limited diverse datasets, black-box interpretability, and overfitting risk that hinder reliable clinical adoption.
Generalizability is limited by dataset scarcity and model opacity, with risk of overfitting on narrow training data.
Evidence
- Peer-reviewedInternational Journal of Biomedical Imaging2026-10-03
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Truvace Impact Record TRV-2026-1281, v1: “Advancing Ultrasound Beamforming With Deep Learning: A Comprehensive Review of Methods, Datasets, Benchmarks, and Computational Challenges.” Truvace, 2026-10-05. /record/TRV-2026-1281 (accessed at citation time). sha256 ccbd407f44b3aeb0…
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