Enhancing Channel Data Savings and Information Transfer Efficiency in Ultrasound Imaging
Bibliographic record
Abstract
Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to advent of plane wave imaging technique in ultrasound, the frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a fraction of seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work will minimize these issues. We proposed and implemented: (a) Data encoding technique - We will combine every two consecutive raw channel data frames without repetition in full data to generate a reduced version of raw channel data, whose size is half of original data in the front end, (b) Data compression technique – We apply discrete frequency domain transforms on full raw data, all frequency components less than or equal to median value are discarded. Both (a) and (b) will reduce data storage by up to 50%. In addition to this, transferring this reduced data into the computer means increasing data transfer efficiency by 50%, when compared to that of full data. Data decoding and inverse frequency domain transforms were performed in the computer software, followed by ultrasound image generation process. Our final technique (c) will integrate both (a) and (b), therefore the overall savings in this technique can reach up to 75%. All these savings can be achieved by slightly increasing the processing time of computer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".