Numerical Investigation of Ultrasound-Triggered Microbubble Contrast Agent Dynamics
Bibliographic record
Abstract
Biomedical ultrasound is widely employed as an imaging modality for anatomical assessment and to provide information on blood flow characteristics. There is increasing interest in employing microbubble contrast agents for diagnostic and therapeutic ultrasound. Unlike MR and CT agents, ultrasound contrast agents are comparable in size to a red blood cell, providing a purely intravascular agent for clinical radiology. Microbubbles are currently clinically employed in echocardiography and liver applications, as well as pre-clinically, for the tumors' characterization and quantifying perfusion. Critical to the effectiveness of contrast agent microbubbles is an understanding of their nonlinear vibrations and scattering within the vasculature, specifically within the microvasculature where standard ultrasound flow estimation suffers from slow blood velocity and low red blood cell concentration. Using mainly a finite element computational approach, this thesis aims to investigate the nonlinear physics of ultrasound-stimulated microbubbles within small capillaries to shed some light on the vibration dynamic and behavior of microbubble contrast agents. Over three chapters of results, this thesis analyses the complex vibration dynamics of microbubbles in proximity to each other and confined in a viscoelastic vessel. The results provided in this thesis explain how the resonance behavior of a microbubble is dampened and shifted by its neighboring bubbles and how smaller bubbles show off-resonance activities corresponding to the resonance behavior of the bigger, neighboring bubbles. The results also explain how initial phospholipid packing and bubble proximity affect subharmonic response and how a viscoelastic vessel dampens resonance behavior and amplifies off-resonance behavior. This thesis conducts a robust study on ultrasound-stimulated microbubble-compliant vessel interactions. It will contribute to optimal contrast agent design for both imaging and therapy, image quantification, and the development of new ultrasound pulse sequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".