Dual-Modal Photoacoustic Imaging and Optical Coherence Tomography [Review]
Bibliographic record
Abstract
Optical imaging technologies have enabled outstanding analysis of biomedical tissues through providing detailed functional and morphological contrast. Leveraging the valuable information provided by these modalities can help us build an understanding of tissues’ characteristics. Among various optical imaging technologies, photoacoustic imaging (PAI) and optical coherence tomography (OCT) naturally complement each other in terms of contrast mechanism, penetration depth, and spatial resolution. The rich and unique molecular-specified absorption contrast offered by PAI would be well complemented by detailed scattering information of OCT. Together these two powerful imaging modalities can extract important characteristic of tissue such as depth-dependent scattering profile, volumetric structural information, chromophore concentration, flow velocity, polarization properties, and temperature distribution map. As a result, multimodal PAI-OCT imaging could impact a broad range of clinical and preclinical imaging applications including but not limited to oncology, neurology, dermatology, and ophthalmology. This review provides an overview of the technical specs of existing dual-modal PAI-OCT imaging systems, their applications, limitations, and future directions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".