Bibliographic record
Abstract
Medical ultrasound imaging requires recurring use of elementary principles, namely, the choice of the transducer, the design of the emitted waves and their wavefronts (e.g. focused or wide), the demodulation of the received signals, their beamforming, and their post-processing to generate B-mode or flow images. The ultrasound signals used for research purposes can be synthetic or acquired. To make the whole pipeline easily accessible to many researchers and students, the objective was to provide an open-access toolbox, widely documented, and adapted to ultrasound imaging research involving experimental or simulation methods. The MUST Matlab UltraSound Toolbox contains algorithms that focus on the development, simulation, and analysis of ultrasound signals for medical imaging. The user can design various ultrasound-imaging scenarios and analyze their performance using simulated or acquired data. The MUST functions allow studying the characteristics of transducers and waveforms, analyze signals, and construct ultrasound images. The many examples provide a starting point for students and researchers to quickly gain an understanding of the essentials of ultrasound imaging. The simulators integrated into MUST provide very realistic acoustic pressure fields and ultrasound images. The Matlab MUST toolbox is freely available at https://www.biomecardio.com/MUST. Before engaging in advanced ultrasound techniques and comparing them with so-called standard methods, it is necessary to have a good understanding of the latter and their advantages and limitations. With this in mind, the MUST toolbox includes everything needed for comprehensive ultrasound imaging: simulators of acoustic pressure fields and backscattered RF signals, delay-and-sum beamforming, B-mode imaging, wall filtering, color or vector Doppler, speckle tracking … The documentation and open access facilitate easy and intuitive use. If the MUST toolbox proves to be interesting, the author plans to integrate advanced features depending on the demands of the ultrasound community.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.082 |
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".