Development of an ultrasound tomography system: Preliminary results
Bibliographic record
Abstract
We are currently developing an ultrasound tomography system at the University of Manitoba. This system consists of eight circular rings of transducers so as to provide the potential to create two- and three-dimensional images. Thirty-two individual piezo-electric transducers are mounted in each ring, and each transducer can both transmit and receive ultrasonic waves. We utilize a frequency-domain inverse scattering framework to invert the measured ultrasound data so as to create quantitative images of the ultrasonic properties of the object being imaged. To this end, the measured data obtained from this system are calibrated before being inverted. For the calibration step, we discuss methods to accurately find the transducers’ positions, properly convert the measured time-domain data into the frequency domain, and then minimize the discrepancy between the actual system and the simulated one through the use of calibration coefficients. Once this process is done, the calibrated frequency-domain data is processed by a frequency-domain inverse scattering algorithm. We utilize the Born iterative method (BIM) to invert this calibrated measured data to create two-dimensional images corresponding to ultrasonic properties of the object of interest [Mojabi & LoVetri, JASA, 2015]. Preliminary results of this BIM inversion are then presented and discussed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".