A Polymer-Based CMUT Probe for Imaging the Spinal Cord in Rats
Bibliographic record
Abstract
The ability of ultrasound imaging to deliver real-time visualization of tissue structures and surgical instruments can provide essential benefits in guiding medical interventions. In spinal cord injury research, small animal models are commonly used, but their size restricts the applicability of many standard ultrasound systems. Capacitive micromachined ultrasonic transducers (CMUTs) offer advantages over traditional piezoelectric transducers, including a smaller form factor, high design flexibility, and improved acoustic performance. CMUT structures made of polymers (polyCMUTs) can be produced cost-effectively and quickly, while potentially offering flexible, biocompatible transducers for next-generation ultrasound systems. This study introduces the first polyCMUT probe designed for imaging spinal cords in rats. A compact 11 MHz, 64 channel probe with a $12.9\times 6.5$ mm small tip was developed through a three-stage fabrication process, combining in-house manufactured polyCMUT arrays with electronics, integrated in a research imaging system. Performance evaluation included electrical impedance measurements, acoustic characterization, and in vitro and ex-vivo imaging. Quality analysis validated the stability of the fabrication process, demonstrating high yield and minimal variability, with a standard deviation in resonance frequency of less than 1%. The probe successfully visualized key anatomical structures like the central canal as well as real-time imaging of needle insertion into tissue. However, distinguishing between gray and white matter remained challenging due to limitations in frequency and bandwidth. This study demonstrates the potential of the polyCMUT technology for developing tailored ultrasound solutions. Future work will focus on optimizing high-frequency performance and advancing toward in vivo applications to provide meaningful tools in spinal cord injury research and therapeutic interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".