Long lived intralipid-infused tissue phantoms: control and characterization of scattering characteristics (Conference Presentation)
Bibliographic record
Abstract
Building long lived test phantoms that simulate the scattering characteristics of biological tissue is needed in for testing optical methods targeted at imaging through tissue. Test phantoms are needed as real tissue samples optical characteristics (scattering coefficients µs and anisotropy factor g) varies greatly between samples and change rapidly with time. Our ongoing work has created long term stable phantoms with lifetimes of more than 6 years and maintaining consistent optical characteristics which mimic skin characteristics. These stable test phantom are created by an intralipid-infused agar layers 1 to 6 mm thickness Varying the intralipid concentration allows control of the scattering parameters with typical values of µs = 20/cm, g = 0.95. By encapsulating the intralipid-infused agar within a clear polymer it stabilizes for long lifetimes and allows creation a varying thicknesses, scattering characteristics and shapes. To characterize these test phantom we developed an enhanced technique where the scattered light from a laser beam passing through the test phantom is captured using a 36x24mm digital camera sensor to capture. This gives over 6 million measurements over a +/- 12 degree range, with typically 20,000 measurements at 2300 angular bins of 0.005 deg. For analysis these measurements of scattering values at a wide range of angles used a Matlab program to identify the scattering center and the angular positions. Fitted scattering models extracted the µs and g parameters for each test phantom Consistent results were obtained using a Henyey-Greenstein two-term model, probably because the Agar and intralipid impacted the scattering separately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".