Capacitive micromachined ultrasound transducers for intravascular ultrasound imaging
Bibliographic record
Abstract
Intravascular ultrasound (IVUS) is a burgeoning imaging technology that provides vital information for the diagnosis of coronary arterial diseases. A significant constituent that enables the IVUS system to attain high-resolution images is the ultrasound transducer, which acts as both a transmitter that sends acoustic waves and a detector that receives the returning signals. Being the most mature form of ultrasound transducer available in the market, piezoelectric transducers have dominated the field of biomedical imaging. However, there are some drawbacks associated with using the traditional piezoelectric ultrasound transducers such as difficulties in the fabrication of high-density arrays, which would aid in the acceleration of the imaging speed and alleviate motion artifact. The advent of microelectromechanical system (MEMS) technology has brought about the development of micromachined ultrasound transducers that would help to address this issue. Apart from the advantage of being able to be fabricated into arrays with lesser complications, the image quality of IVUS can be further enhanced with the easy integration of micromachined ultrasound transducers with complementary metal-oxide-semiconductor (CMOS). This would aid in the mitigation of parasitic capacitance, thereby improving the signal-to-noise. Currently, there are two commonly investigated micromachined ultrasound transducers, piezoelectric micromachined ultrasound transducers (PMUTs) and capacitive micromachined ultrasound transducers (CMUTs). Currently, PMUTs face a significant challenge where the fabricated PMUTs do not function as per their design. Thus, CMUTs with different array configurations have been developed for IVUS. In this paper, the different ultrasound transducers, including conventional-piezoelectric transducers, PMUTs and CMUTs, are reviewed, and a summary of the recent progress of CMUTs for IVUS is presented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".