A CT based correction method for speed of sound aberration for ultrasound based image guided radiotherapy
Bibliographic record
Abstract
PURPOSE: To introduce a correction for speed of sound (SOS) aberrations in three dimensional (3D) ultrasound (US) imaging systems for small but systematic positioning errors in image guided radiotherapy (IGRT) applications. US waves travel at different speeds in different human tissues. Conventional US-based imaging systems assume that SOS is constant in all tissues at 1540 m/s which is an accepted average value for soft tissues. This assumption leads to errors of up to a few millimeters when converting echo times into distances and is a source of systematic errors and image distortion in quantitative US imaging. METHODS: At simulation, US applications for IGRT provide a computed tomography (CT) image coregistered to a US volume. The CT scan provides the physical density which can be used in an empirical relationship with SOS. This can be used to correct for different SOS in different tissues within the patient. For each US scan line each voxel's axial dimension is rescaled according to the SOS associated to it. This SOS correction method was applied to US scans of a PMMA container filled with either water, a 20% saline water solution or sunflower oil, and the results were compared to the CT. The correction was also applied to an US quality assurance (QA) phantom containing rods with high ultrasound contrast. This phantom was scanned with US through a container filled with the same three liquids. Finally, the algorithm was applied to two clinical cases: a prostate cancer patient and a breast cancer patient. RESULTS: After the correction was applied to the phantom images, spatial registration between the bottom of the phantom in the US scan and in the CT scan was improved; the difference was reduced from a few millimeters to less than one millimeter for all three different liquids. Reference structures in the QA phantom appeared at more closely corresponding depths in the three cases after the correction, within 0.5 mm. Both clinical cases showed small shifts, up to 3 mm, in the positions of anatomical structures after correction. CONCLUSIONS: The SOS correction presented increases quantitative accuracy in US imaging which may lead to small but systematic improvements in patient positioning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 0.002 |
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".