Mathematical Modeling of the Vaporization of Encapsulated Perfluorocarbon Nanodroplets using Chirp Ultrasound
Bibliographic record
Abstract
Ultrasound imaging is the use of sound waves beyond the human frequency range to construct images of human tissue. This is carried out through the measurement of reflected and scattered waves from the interfaces between tissues of differing acoustic impedances. Conventional ultrasound imaging faces limitations when imaging tissue microvasculature, resulting in poor resolution between blood and the surrounding tissue. This can be remedied using microbubbles which oscillate under ultrasound stimulation and thus provide an enhanced backscattered signal. Free gas bubbles however have a short half-life in the bloodstream as they are removed by the lungs. Therefore, phase-change contrast agents (PCCAs) have been introduced as an alternative which has better longevity in the body. An example of a PCCA is encapsulated perfluorocarbon nanodroplets. These nanodroplets are in liquid form when introduced into the body, but under exposure to ultrasound waves in the target tissue, undergo vaporization to form micrometer-scale bubbles. Therefore achieving a similar level of contrast enhancement to microbubbles. The encapsulation also confers them increased stability in circulation. \n \nTypical use of ultrasound in medical imaging involves pulses of several wave cycles at constant frequency. Higher frequency ultrasound results in better axial resolution but a reduced penetrative depth as it undergoes a larger degree of attenuation within tissues. Coded excitation schemes where the outgoing ultrasound waveform is either frequency-modulated or phase-modulated can be utilized to increase axial resolution without sacrificing transmitted power and penetrative depth. One example of a coded excitation scheme is a linear chirp where the frequency of the ultrasound pulse increases linearly from the beginning to the end of the pulse. \n \nThe acoustic droplet vaporization of encapsulated perfluorocarbon nanodroplets under chirp ultrasound was investigated and it was found that although the increase in frequency over the course of the ultrasound pulse inhibits direct vaporization, if the stiffness of the encapsulating shell can be kept relatively low, there are feasible ultrasound parameters (amplitude, starting frequency and chirp bandwidth) which can still cause direct vaporization. This represents an improvement since the nanodroplets still fulfill their role as phase-change contrast agents and the chirp ultrasound fulfills its role of enhancing the axial resolution of the image.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".