The Role of Microbubble Echo Phase Lag in Multipulse Contrast-Enhanced Ultrasound Imaging
Bibliographic record
Abstract
In this paper, we assess the importance of microbubble shell composition for contrast-enhanced imaging sequences commonly used on clinical scanners. While the gas core dynamics are primarily responsible for the nonlinear harmonic response of microbubbles at diagnostic pressures, it is now understood that the shell rheology plays a dominant role in the nonlinear response of microbubbles subjected to low acoustic pressures. Of particular interest here, acoustic pressures of tens of kilopascal can cause a reversible phase transition of the phospholipid coatings from a stiff elastic organized state to a less stiff disorganized buckled state. Such a transition from elastic to buckled shell induces a steep variation of the shell elasticity, which alters the microbubble acoustic scattering properties. We demonstrate in this paper that this mechanism plays a dominant role in contrast pulse sequences that modulate the amplitude of the insonifying pulse pressure. The contrast-to-tissue ratio (CTR) for amplitude modulation (AM), pulse inversion (PI), and amplitude modulation pulse inversion (AMPI) is measured in vitro for Definity, Sonazoid, both lipid-encapsulted microbubbles, and the albumin-coated Optison. It is found that pulse sequences using AM significantly enhanced the nonlinear response of all studied microbubbles compared to PI (up to 15 dB more) when low insonation pressures under 200 kPa were used. Further investigation reveals that the origin of the hyperechoicity is a small phase lag occurring between the echoes from the full-and half-amplitude driving pulses, and that the effect could be attributed to the shell softening dynamics of lipid and albumin coatings. We assess that this additional phase in microbubble ultrasound scattering can have a dominant role in the CTR achieved in contrast sequences using AM. We also show that the pressure dependent phase lag is a specific marker for microbubbles with no equivalent in tissue, which can be used to segment microbubbles from the tissue harmonics and significantly increase the CTR.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".