A speed of sound aberration correction algorithm for curvilinear ultrasound transducers in ultrasound-based image-guided radiotherapy
Bibliographic record
Abstract
Conventional ultrasound (US) devices use the time of flight (TOF) of reflected US pulses to calculate distances inside the scanned tissues and thus create images. The speed of sound (SOS) is assumed to be constant in all human soft tissues at a generally accepted average value of 1540 m s(-1). This assumption is a source of systematic errors up to several millimeters and of image distortion in quantitative US imaging. In this work, an extension of a method recently published by Fontanarosa et al (2011 Med. Phys. 38 2665-73) is presented: the aim is to correct SOS aberrations in three-dimensional (3D) US images in those cases where a spatially co-registered computerized tomography (CT) scan is also available; the algorithm is then applicable to a more general case where the lines of view (LOV) of the US device are not necessarily parallel and coplanar, thus allowing correction also for US transducers other than linear. The algorithm was applied on a multi-modality pelvic US phantom, scanned through three different liquid layers on top of the phantom with different SOS values; the results show that the correction restores a better match between the CT and the US images, reducing the differences to sub-millimeter agreement. Fifteen clinical cases of prostate cancer patients were also investigated: the SOS corrections of prostate centroids were on average +3.1 mm (max + 4.9 mm-min + 1.3 mm). This is in excellent agreement with reports in the literature on differences between measured prostate positions by US and other techniques, where often the discrepancy was attributed to other causes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".