Realtime Ultrasound Prostate Young’s Modulus Reconstruction Technique Using a Full Inversion Approach
Bibliographic record
Abstract
In this work, we present a real-time prostate elastography reconstruction technique which incorporates an accelerated method of tissue stress calculation in its algorithms. The accelerated FE method uses a database of prostate-tumor configurations obtained from imaging. These configurations undergoing specific mechanical loading, e.g. US probe, are modeled and analyzed using conventional FEM to obtain the corresponding stress fields. Principal component analysis (PCA) is used to obtain the main modes of shape and stress fields. As such, the shape and stress fields can be described by these main modes weighted by a small number of weight factors. Next, an efficient mapping technique is developed to relate the weight factors of shape to those of the stress fields. We used Artificial Neural Network (ANN) and PCA based regression for this mapping. Once the mapping function is obtained it can be used for analyzing prostate shapes not included in the database. For a typical prostate, our results indicate that analysis using our technique takes less than 0.1 seconds on a desktop computer irrespective of the model size, while the maximum stress error is less than 5% per element. This mapping is then used for our novel real-time Young’s modulus reconstruction technique in which prostate and tumor moduli are updated iteratively using strain images acquired from an ultrasound imaging system and stress field estimated with the proposed method. Results of elastography show that relative Young’s moduli can be reconstructed fairly accurately.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".