GPU technology in the high frame rate ultrasound imaging era
Bibliographic record
Abstract
As a rapidly maturing class of many-core parallel computing hardware, graphical processing unit (GPU) has enjoyed a surge of interest in the medical ultrasound community in the past decade. In particular, the software-level programmability of GPUs has significantly lowered the entry barrier for ultrasound imaging researchers (who might not be parallel computing specialists) to pursue fast realization of novel imaging algorithms that have known theoretical potential but have yet to demonstrate their real-time feasibility. This presentation shall highlight how GPUs have emerged as a new computing workhorse in realizing various ultrasound imaging innovations. Of particular note is the enabling role that GPUs have played in fostering practical pursuit of high-frame-rate ultrasound (HiFRUS) imaging innovations that are based on direct processing of pre-beamformed radiofrequency (RF) data acquired from individual array elements. Using the state-of-art GPU technology, it is readily possible to achieve >1,000 fps HiFRUS beamforming throughput. GPU can also play a pivotal role in facilitating fast realization of computationally intensive HiFRUS algorithms, such as adaptive beamforming, color-encoded flow speckle imaging, and eigen-processing. The real-time performance of these GPU computing kernels will be discussed, and their practical implementation on software-oriented open-platform research systems will be presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".