Efficient Data Acquisition and Reconstruction in Ultrasound Imaging
Bibliographic record
Abstract
Ultrasound is a prominent imaging technique used in a variety of applications. Due to very high frame rate, the amount of raw data obtained while using an ultrasonic device is large. Thus, data management and storage are posing significant problems. To address these issues, we proposed and implemented a few efficient data acquisition, reconstruction, filtering, and noise removal techniques. These methods reduce the amount of raw data collection via hardware devices, followed by missing data reconstruction in the software using a variety of digital signal processing techniques such as Spline interpolation, Discrete Cosine Transform (DCT) and Inverse Discrete Cosine Transform (IDCT), Discrete Sine Transform (DST) and Inverse Discrete Sine Transform (IDST) etc., This reconstructed data is sent for Frequency Domain (FD) beamforming (a recently proposed beamforming technique) and post-processing to generate output images. For comparison, all outputs were generated using full data and Delay-and-Sum (DAS) beamforming (traditional beamforming technique in ultrasound). From which, it was clear that we can reduce the data storage cost by 33.3% and 50%, by increasing the software’s operation time by a few seconds (i.e. less than one minute).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".