The use of microbubbles as contrast agents in the optical imaging of tissue
Bibliographic record
Abstract
In this study, micrometer-sized (< 5μm) microbubbles (MBs) were investigated as optical contrast agents. Exogenous contrast agents in optical imaging can enhance the detection of cancerous tissue, however they are limited by their potential side effects such as organ accumulation and toxicity with repeated administration. In ultrasound imaging, microbubbles are FDA approved and used clinically as contrast agents. Microbubbles can also produce a refractive index mismatch between the gas-filled core and surrounding water media, potentially modifying optical properties of tissue when injected intravenously into tissue. Cancer detection can potentially be achieved by imaging microvasculature functionality through the blood volume fraction measurements with diffuse optical imaging (DOI). In order to study the potential application of using MBs for DOI, the effect of MBs on the bulk optical properties of a skin tissue phantom solution at various volume fraction of human blood was assessed at various injection doses of Definity® microbubble. The absorption and reduced scattering coefficients were computed and compared in the absence and presence of microbubbles. The presence of microbubbles in the blood caused a statistically significant enhancement in the reduced scattering contrast (~1.3 times) at 660 nm wavelength which increases with the dose of Definity® MBs (166 μL/kg) at 6% blood volume fraction. However, the absorption contrast enhancement remained relatively constant as microbubble dose increased. The scattering contrast enhancement confirmed the feasibility of using MBs as DOI contrast agents to improve the detection of tissue with high blood concentration conditions. Microbubbles were also investigated as optical coherence tomography (OCT) contrast agents. OCT structural and speckle variance (sv) images, as well as the speckle decorrelation times, were evaluated under no-flow and flow conditions from a skin tissue phantom with two embedded microtubes. Faster decorrelation times and greater structural and svOCT image contrasts were detected with the presence of MBs. The effects of the presence of MBs on the image contrast were maximal (2 times) at no flow in the svOCT imaging mode and reduced with blood average flow velocity from zero to 12 mm/s. This result confirmed the feasibility of using MBs to enhance svOCT visualization of microvasculature morphology.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".