Direct strain estimation in ultrasound elastography using a novel dynamic programming approach
Bibliographic record
Abstract
Quasi-static elastography methods often calculate the displacement field from ultrasound data and calculate strain by performing spatial derivative of the displacement field. In this paper, a strain imaging technique called SHORTCUT is introduced in which the strain field is estimated directly from the RF data using a novel dynamic programming (DP) technique. The DP cost function is formulated in terms of strain and incorporates similarity of echo amplitudes and strain continuity into a cost function. This is in contrast to previous work wherein the cost function was formulated in terms of displacement and enforced displacement continuity. This approach has several advantages. First, a much smaller search range for the displacement derivative will cover a much larger search range of the displacement field. This will substantially reduce the computational complexity of DP. Second, the new framework substantially reduces the bias introduced by the displacement continuity constraint. And third, the strain is directly estimated from DP and no spatial derivation step is needed. Our results on phantom and in vivo patient data show that SHORTCUT substantially outperforms previous work.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".