Review on photoacoustic imaging of the brain using nanoprobes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it