Blind Deconvolution of Fundamental and Harmonic Ultrasound Images
Bibliographic record
Abstract
Restoring the tissue reflectivity function (TRF) from ultrasound (US) images is an extensively explored research field. It is well-known that human tissues and contrast agents have a non-linear behavior when interacting with US waves. In this work, we investigate this non-linearity and the interest of including harmonic US images in the TRF restoration process. Therefore, we introduce a new US image restoration method taking advantage of the fundamental and harmonic components of the observed radiofrequency (RF) image. The depth information contained in the fundamental component and the good resolution of the harmonic image are combined to create an image with better properties than the fundamental and harmonic images considered separately. Under the hypothesis of weak scattering, the RF image is modeled as the 2D convolution between the TRF and the system point spread function (PSF). An inverse problem is formulated based on this model able to jointly estimate the TRF and the PSF. The interest of the proposed blind deconvolution algorithm is shown through an in vivo result and compared to a conventional US restoration method.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".