Pulse Inversion Imaging of Liver Blood Flow
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: To create a microbubble contrast image of vessels that lie below the resolution of an ultrasound system, a technique is required that detects preferentially the agent echo, rejecting that from tissue. Harmonic imaging exploits the nonlinear behavior of microbubbles but forces a compromise between image sensitivity and axial resolution. The authors describe and evaluate a new method that overcomes this compromise and improves contrast imaging performance: pulse inversion imaging. METHODS: Sequences of pulses of alternate phase are transmitted into tissue and their echoes summed. A prototype scanner equipped with pulse inversion was used to image phantoms and 16 patients with focal liver masses. RESULTS: Pulse inversion images show contrast sensitivity and resolution superior to that of harmonic images. Vessels can be imaged at an incident power sufficiently low to avoid destroying the agent, allowing unique visualization of tumor vasculature. Distinct patterns were seen in hemangiomas, metastases, and hepatocellular carcinomas. CONCLUSIONS: Pulse inversion imaging is an improved bubble-specific imaging method that extends the potential of contrast ultrasonography.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".