Ultra-High Frequency Photoacoustic Microscopy: From Organelles to Organisms
Bibliographic record
Abstract
This dissertation describes novel signal analysis and imaging techniques for ultrahigh frequency (UHF, over 100 MHz) Photoacoustic Microscopy (PAM). New approaches for extracting information pertaining to object structure and scale are described, and novel sensing techniques and contrast mechanisms for imaging biological samples ranging from single cells to small organisms are presented. In the first section, I describe a methodology for assessing the structure of biological cells using UHF-PAM. The power spectra of ultrasound (US) pulses backscattered from MCF-7 cells, and photoacoustic (PA) waves emitted from their dyed nuclei were fit to analytical solutions to determine cell and nucleus diameter, respectively. The measured cell diameters (15.5±1.8 μm) and nucleus diameters (12±1.3 μm) were used to calculate the mean cell nucleus-to-cytoplasm ratio (1.9±1.0). Good agreement was observed between UHF-PAM measured values and literature. In the second section, I present a novel technique for PA image reconstruction that utilizes unique features in the PA power spectra as a source of contrast. The technique, termed F-Mode, provides a means for differentiating between objects of different scale that surpasses the capabilities of conventional reconstruction approaches. The ability of F-Mode to selectively accentuate absorbers of different size was demonstrated using experimental phantoms containing microspheres and cylindrical vessels, as well as in individual biological cells and live zebrafish larvae. Finally, I developed a new sensing technique, termed Photoacoustic Radiometry (PAR). Unlike PAM, which depicts optical absorption, PAR images depict the optical attenuation properties of the imaged object. It was demonstrated that PAR can be used to image transparent samples which generate no PA signals, and that simultaneous triplex PAR/PA/US imaging could be realized using our approach. Simultaneous PAR/PA imaging of biological cells, as well as zebrafish larvae in vivo, was also demonstrated. UHF-PAM provided excellent visualization of vascular organization in the larval trunk and head. The simultaneously acquired PAR images depicted anatomical structure (e.g. the notochord, muscle segments) not visible in PAM due to insufficient optical absorption. Potential areas of application for the new UHF-PAM techniques described in this dissertation include detection of cancer cells in blood samples, and investigation of tumour growth and metastasis. (6 zipped .mp4 files) https://digital.library.ryerson.ca/islandora/object/RULA:8618/datastream/Movies_Moore_mp4/view
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".