Application of Radon Transform to Wavefield Filtering, Reconstruction, and Imaging of Dispersive Energies in Quantitative Bone Ultrasound
Bibliographic record
Abstract
Multichannel ultrasonic axial-transmission data are multimodal by nature. Multichannel analysis of dispersive ultrasonic energies requires a reliable mapping of the data from the time– distance (t-x) domain to the frequency–wavenumber (f-k) or frequency–phase velocity (f-c) domain. The mapping is usually performed with the classical 2-D Fourier transform (FT) from t- x plane to f-k plane or to f-c plane with a subsequent substitution and interpolation via c=ω/k. The extracted dispersion trajectories of the guided modes lack the resolution in the transformed domain to discriminate wave modes. The resolving power associated with the FT is closely linked to the aperture of the recorded data. In this presentation, we present a linear high- resolution Radon transform (RT) algorithm [1 & 2] to filter and reconstruct wavefields, and to image the dispersive energies of the recorded wavefields through long bones. The RT is posed as an inverse problem, which allows implementation of the regularization strategy to enhance the focusing power. The simulated, ex-vivo, and in-vivo data will be used to illustrate the advantages and robustness of the high-resolution RT algorithm. The method accommodates unevenly spaced records, effectively attenuates noise, enhances the signal-to-noise ratio, improves the coherency of the guided wave modes, and reconstructs the missing records. The dispersive energies are well focused and the trajectories are much better resolved. The proposed transform presents a powerful signal enhancement and imaging tool to process ultrasonic wavefields and extract dispersive guided wave energies under limited aperture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".