Blind deconvolution of medical ultrasound images using variable splitting and proximal point methods
Bibliographic record
Abstract
The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, a hybrid deconvolution method is employed to recover a reliable estimate of the tissue reflectivity function directly from ultrasound RF data. Here, the “hybridization” suggests a two-stage reconstruction scheme, in which some partial information about the point spread function (PSF) of the imaging system is recovered first, followed by its use to explicitly constrain the procedure of inverse filtering. The latter is realized in the form of an optimization problem, whose efficient and stable solution is addressed in this note. In particular, we proposed to solve the problem of inverse filtering using the alternating direction method of multipliers (ADMM). We show that this method leads to a particularly efficient numerical scheme, which is implemented as a succession of analytically computable proximity operations. Additionally, it is shown how the inverse filters designed in this way can be used to deconvolve the ultrasound images in a non-blind manner so as to further improve their resolution and contrast. The effectiveness of the proposed deconvolution procedures is exemplified by in vivo experiments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".