Ultrasonic Methods to Measure Shear Wave Velocity and Absorption for Tissue Viscoelasticity Characterization
Bibliographic record
Abstract
Characterization of the viscoelasticity for soft tissues is useful for medical diagnosis.Various ultrasound and magnetic resonance-based techniques have been developed over the past few decades.With ultrasound methods, generating and tracing a shear wave (SW) propagating within the soft tissue was one of the promising approaches to measure the SW velocity and absorption, which were the key parameters to derive the tissue viscoelasticity.When the SW method was employed, several problems, such as undesired SW reflection, motion artifact and SW diffraction could occur in practical experimental configuration, which caused on SW measurements.Advanced ultrasound imaging system with high frame rate may be able to overcome these problems, yet the configuration of such system was complex and thus expensive.In this thesis, methods to solve the abovementioned problems were proposed by using conventional ultrasound imaging system with a relatively less complex design and low frame rate.In the proposed methods, the undesired SW reflections were able to be distinguished and removed from the observed SW in the spatial domain using the scanning time delay in the B-mode measurement of conventional ultrasound system.The causes of the motion artifact were examined through the temporal domain analysis, and temporal frequency filter was used to reduce the undesired motions.In addition, the measurement configuration to minimize the diffraction effect on the SW measurement was investigated.Furthermore, a numerical method to compensate the diffraction effect was developed for the absorption measurement.The effectiveness of these proposed methods was verified by numerical
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".