Review on photoacoustic imaging of the brain using nanoprobes
Bibliographic record
Abstract
Photoacoustic (PA) tomography (PAT) is a hybrid imaging modality that integrates rich optical contrasts with a high-ultrasonic spatial resolution in deep tissue. Among various imaging applications, PA neuroimaging is becoming increasingly important as it nicely complements the limitations of conventional neuroimaging modalities, such as the low-temporal resolution in magnetic resonance imaging and the low depth-to-resolution ratio in optical microscopy/tomography. In addition, the intrinsic hemoglobin contrast PA neuroimaging has also been greatly improved by recent developments in nanoparticles (NPs). For instance, near-infrared absorbing NPs greatly enhanced the vascular contrast in deep-brain PAT; tumor-targeting NPs allowed highly sensitive and highly specific delineation of brain tumors; and multifunctional NPs enabled comprehensive examination of the brain through multimodal imaging. We aim to give an overview of NPs used in PA neuroimaging. Classifications of various NPs used in PAT will be introduced at the beginning, followed by an overview of PA neuroimaging systems, and finally we will discuss major applications of NPs in PA neuroimaging and highlight representative studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".