Accelerator Architecture for Plane-Wave Ultrasound Image Reconstruction in Fourier Domain
Bibliographic record
Abstract
Ultrafast ultrasound imaging based on coherent plane-wave compounding (CPWC) enables very high data acquisition rates in the order of thousands of frames per second. This capability allows the user to capture and characterize fast-changing dynamics of blood flow or tissue motion, thus facilitating advanced biomedical diagnostics. Fast data acquisition should be supported by high image reconstruction rates, which translates into significant computational demands. To address this issue, several state-of-the-art hardware accelerators for CPWC image reconstruction, or beamforming, have been reported in the literature. They primarily target time-domain methods based on delay-and-sum (DAS) beamforming. For the first time, this article proposes a novel hardware architecture for accelerating Fourier-domain image reconstruction, based on an efficient migration technique from geophysics. Our FPGA implementation of one specific architectural instance achieves the reconstruction throughput of 1,380 frames per second (without compounding), where each complex-valued “analytic” image frame consists of$2048\times 128~64$-bit data samples. The presented work also aims to motivate further research into hardware support for Fourier-domain migration. This technique is asymptotically faster than conventional DAS beamforming; however, its efficient hardware realization is challenging, partly due to its relatively large memory footprint.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".