In vivo localized harmonic motion imaging of VX2 tumors
Bibliographic record
Abstract
We evaluated the feasibility of localized harmonic motion (LHM) imaging for tumor detection in vivo. LHM was induced using a single-element focused ultrasound (FUS) transducer (80 mm focal, 100 mm diameter, 1.54 MHz) and a separate transducer (5 kHz PRF, 5 MHz) was used to track motion by cross-correlating RF signals. A scan was performed with the transducers assembly and LHM was induced 5 times per location. Images were formed averaging the calculated LHM amplitudes. Ten New Zealand rabbits had VX2 tumors implanted on their thighs. Tumors were located using Magnetic resonance images and LHM images were obtained. Eight out of ten tumors were visualized on LHM images as a region with lower amplitude (5.7±1.3μm in tumors and 19.5±5.8μm in muscle). All tumors had an elongated shape running along the muscle fibers. It was possible to detect tumors larger than 4mm in width (short axis of the tumor). We performed a FUS ablation of one tumor and the ablated region was detected as well on LHM images as a reduced LHM amplitude region.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".