Modelling high frequency ultrasound scattering from cells and ultrasound contrast agents
Bibliographic record
Abstract
High frequency ultrasound has been shown to detect structural and physical changes in cell ensembles during apoptosis and hence has the potential of monitoring cancer treatment. Ultrasound contrast agents have also been shown to enhance contrast between blood and the surrounding tissue and hence may be used to distinguish between treated and untreated tumours. Theoretical models of high frequency ultrasound scattering from individual cells and ultrasound contrast agents (UCAs) are needed in order to develop methods for using high frequency ultrasound to classify tumours, quantify their responses to treatment, and eventually provide a better cancer detection and treatment monitoring techniques. This work introduces a new technique for measuring the ultrasound backscatter from individual micron-sized objects by combining a microinjection system with a co-registered optical microscope and an ultrasound imaging device. The system was calibrated by measuring the backscatter response from polystyrene microspheres and comparing it to theoretical predictions of an elastic sphere. The backscatter responses from single sea urchin oocytes and acute myloid leukemia cells were also investigated. It was found that such responses are best modelled using the fluid sphere model. A finite element model was also introduced to study scattering from microspheres and UCAs. The Helmholtz equation was used to describe the propagation of sound waves in the fluid domains whereas the constitutive equation was used to describe the stress-strain relationship in the solid domains. Studies on polystyrene microspheres and UCAs revealed the existence of a systematic relationship between the resonance frequencies and the microsphere surface modes. No such a relationship was found for the UCAs of interest. Instead, these agents exhibited a collection of complex oscillations which appear to be a combination of various surface modes. Increasing the UCA's shell thickness and its shear modulus produced a shift in the resonance frequencies to higher values. A decrease in UCA diameter produced similar effects. The importance of these findings towards the understanding of the UCA behaviour at high frequencies and the generation of harmonics are discussed. Future work includes the measurement of the backscatter response from individual UCAs and cells at various apoptotic stages.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".