Optical Scattering Properties of Intralipid Phantom in Presence of Encapsulated Microbubbles
Bibliographic record
Abstract
In imaging, contrast agents are utilized to enhance sensitivity and specificity of diagnostic modalities. In ultrasound imaging, microbubbles (MBs)—a gas-core shell-encapsulated agent—are used clinically as contrast agents. The working hypothesis of this study is that microbubbles can be employed as an intravascular contrast agent in optical imaging systems. In this work, the interaction of light and microbubbles in a turbid medium (intralipid) was investigated, particularly, the effect of MBs on the reduced scattering and absorption coefficients. Diffuse reflectance (DR) and total transmittance (TT) measurements of highly scattering intralipid suspension (0.5–5%) were measured using spectroscopic integrating sphere system in the absence and presence of Definity microbubbles. The optical properties were computed using the inverse adding doubling (IAD) software. The presence of microbubbles increased DR and decreased TT of intralipid phantoms. In the presence of MBs (0.5% volume concentration), the reflectance of the intralipid phantom increased from 35% to 100%. The reduced scattering coefficient increased significantly (30%) indicating potential use of MBs as optical contrast agents in light based modalities.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".