Examination of suitable material for phantoms used in photoacoustics
Bibliographic record
Abstract
A fundamental part of modern medicine is the ability to study and analyze images, especially for diagnostic purposes. A relatively new imaging system is photoacoustics, where laser- and ultrasound technology are combined to create images in a non-invasive way. It is absorption of the laser that is responsible for the creation of the ultrasound signal. Thus, it is the optic properties of tissue that enable differentiating. Normally it is the amplitude of the returning signal which is utilized for image contrasting in photoacoustics. Instead, this study has examined the center frequency to wavelength dependence of the returning signal. Two parameters assumed to have an effect on the center frequency spectrum are the size and colour of particles. The aim of this study was to investigate whether microsphere phantoms can be used to confirm this assumption. Phantoms consisting of microspheres in different sizes in the colours blue, green and transparent were created. The photoacoustic system at Lund University Hospital (VisualSonics Vevo LAZR-X, Toronto, Canada) was used to make nine measurements, during three different days, for each one of these. After analysis of the data from the amplitude of the returning signal, it was concluded that the spheres in blue and green are appropriate for future research in this field. The results from analyzing the mean and standard deviation of the center frequency was that there is a nonlinear correlation between sphere size and center frequency. There was also a distinct difference between the center frequency spectrum for the blue and green spheres.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".