Development of optical coherence tomography technique for clinical diagnostics and monitoring
Bibliographic record
Abstract
This dissertation describes techniques that use Optical Coherence Tomography techniques developed for the detection of shear wave propagation in different phantoms, and the use of such waves to enhance the transport of nanoparticles in tissue equivalent phantoms. In the first study, we explored the potential of measuring shear wave propagation using optical co-herence elastography (OCE) in an inhomogeneous phantom and carotid artery samples based on a swept source optical coherence tomography (OCT) system. Shear waves were generated using a piezoelectric transducer transmitting sine-wave bursts of 400 μs duration, applying acoustic radiation force (ARF) to inhomogeneous phantoms and carotid artery samples, syn-chronized with a swept-source OCT (SS-OCT) imaging system. The phantoms were com-posed of gelatin and titanium dioxide whereas the carotid artery samples were embedded in propagating shear waves in inhomogeneous tissue equivalent phantoms and carotid artery samples using the ARF of an ultrasound transducer, and measuring the shear wave speed and its associated properties in the different layers with OCT phase maps. In the second study, we present a technique to image the enhanced particle displacement generated using an acoustic radiation force (ARF) excitation source. A MEMS-VCSEL swept source Optical Coherence Tomography (SS-OCT) system with a center wavelength of 1310 nm, a bandwidth of 100nm, and an A-scan rate of 100 kHz was used to detect gold nanoparticle displacement. ARF was applied after the nanoparticles diffused into a collagen matrix (of different collagen concen-trations and for a tissue engineered MCF-7 breast cancer cell construct). Differential OCT speckle variance images with and without the ARF were used to estimate the particle dis-placement. The images were used to detect the microscopic enhancement of nanoparticle displacement generated by the ARF. Using this OCT imaging technique, the enhanced transport of particles though a collagen gel after using an ARF excitation was imaged and analysed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".