Development of a High Resolution Microvascular Imaging Toolkit for Optical Coherence Tomography
Bibliographic record
Abstract
This thesis presents the development of new optical coherence tomography imaging systems and techniques to improve in vivo 3D microvascular imaging. Specifically these systems and techniques were proposed to address three main problems with 3D Doppler optical coherence tomography imaging: (a) Motion artefacts, (b) angle dependence of the signal, and (c) relatively high minimum detectable velocity of conventional color Doppler algorithms (~500 μm/s). In order to overcome these limitations a multi-pronged strategy was employed: (1) Construction of a retrospectively gated OCT system for the mitigation of periodic motion artefacts. Proof of principle in vivo B-mode imaging of Xenopus Laevis (embryo of African clawed frog) cardiovascular function up to 1000 frames per second (fps) from data acquired at 12 fps. Additionally, 4D imaging of the Xenopus Laevis heart at 45 volumes per second was demonstrated. (2) Construction of a Fourier domain mode locked laser for high speed swept source optical coherence tomography imaging. This laser was capable of reaching sweep rates of 67 kHz and was optimized to function in the SNR limited phase noise regimes upto approximately 55 dB structural SNR. (3) Development of a novel speckle variance image processing algorithm for velocity and angle independent 3D microvascular imaging. The velocity and angle independence of the technique was validated through phantom studies. \niii \nIn vivo demonstration of the speckle variance algorithm was performed by imaging the capillary network in the dorsal skin-fold window chamber model, with the results being validated using fluorescence confocal microscopy. In the final part of this thesis, these newly developed technologies were applied to the assessment of anti-vascular and anti-angiogenic therapies in preclinical models, specifically, photodynamic therapy and targeted degradation of HIF-α.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".