Separate emission/reception transducers for 3D ultrafast ultrasound imaging
Bibliographic record
Abstract
Recent advancements in ultrafast 3D ultrasound imaging have revolutionized the field of echography, enabling its extension to novel applications such as cerebral dynamics, cardiac electrophysiology and the quantitative imaging of intrinsic mechanical properties of tumors. To facilitate these advancements, two primary transducer strategies are employed in 3D imaging. The first involves dense 2D probes equipped with a large number of elements, typically exceeding 1024. The second strategy uses row-column addressing, which simplifies the electronic control of the probe elements. Despite their effectiveness, these methods entail complexities in design and fabrication. Addressing these challenges, our study introduces an innovative transducer configuration that distinctly separates emission and reception functionalities. This separation not only simplifies the overall transducer design but also significantly reduces the system's complexity. A sparse array of PVDF transducers, which have been laser micro-machined to ensure acoustic transparency, is used at the reception. For the emission aspect, we employ a specialized acoustic concentrator to emulate point-like emission. The paper presents the detailed design requirements, assembly process, and the operational principles of this novel transducer. Furthermore, an experimental validation is conducted using a CIRS 040GSE phantom model. This validation is crucial to demonstrate the practical applicability and reliability of our transducer in real-world medical imaging scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".