Magnitude of speed of sound aberration corrections for ultrasound image guided radiotherapy for prostate and other anatomical sites
Bibliographic record
Abstract
PURPOSE: The purpose of this work is to assess the magnitude of speed of sound (SOS) aberrations in three-dimensional ultrasound (US) imaging systems in image guided radiotherapy. The discrepancy between the fixed SOS value of 1540 m∕s assumed by US systems in human soft tissues and its actual nonhomogeneous distribution in patients produces small but systematic errors of up to a few millimeters in the positions of scanned structures. METHODS: A correction, provided by a previously published density-based algorithm, was applied to a set of five prostate, five liver, and five breast cancer patients. The shifts of the centroids of target structures and the change in shape were evaluated. RESULTS: After the correction the prostate cases showed shifts up to 3.6 mm toward the US probe, which may explain largely the reported positioning discrepancies in the literature on US systems versus other imaging modalities. Liver cases showed the largest changes in volume of the organ, up to almost 9%, and shifts of the centroids up to more than 6 mm either away or toward the US probe. Breast images showed systematic small shifts of the centroids toward the US probe with a maximum magnitude of 1.3 mm. CONCLUSIONS: The applied correction in prostate and liver cancer patients shows positioning errors of several mm due to SOS aberration; the errors are smaller in breast cancer cases, but possibly becoming more important when breast tissue thickness increases.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".