Directivity-only ultrasound computed tomography based on multiaxial devices
Bibliographic record
Abstract
Ultrasound Computer Tomography (UCT) is a promising medical imaging method for imaging of organs such as the breast and brain. Current UCT image reconstruction methods fall broadly into two categories: Time of Flight (TOF) based ray tracing methods and full-waveform methods. Ray tracing methods are computationally efficient but often have blurred edges and low spatial resolution, while full-waveform methods result in excellent image quality but at high computational cost. To improve the image quality of ray-based methods without greatly increasing computational cost, we propose a bent-ray UCT reconstruction algorithm that uses Direction of Arrival (DOA) rather than TOF information. DOA- and TOF-UCT methods are evaluated in silico using two-dimensional Shepp-Logan and breast tissue phantoms with speeds of sound ranging from 1460m/s to 1580m/s. Both the Shepp-Logan and breast tissue phantoms were imaged with 128-element ring arrays with diameters of 38.5cm and 21cm, operating frequencies of 350kHz and 600kHz, and resolutions of 0.71mm and 0.41mm, respectively. Initial measurements were derived from a finite-difference time-difference solution of the viscoelastic wave equation. We demonstrate that DOA-UCT reconstruct images have sharper edges and better perceived image quality than conventional TOF-UCT using the same imaging array. DOA-UCT is also able to reconstruct images at multiple time points rather than just the initial wavefront. To the best of our knowledge, this is the first demonstration of a bent-ray UCT algorithm that uses only DOA information, without TOF information.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".