Differential Processing of Structured Light Projections for Dynamic Optical Body Surface Sensing during Radiation Therapy
Bibliographic record
Abstract
Complex radiotherapy techniques now rely on the positional integrity of the patient. Limited radiological image guidance for internal targeting is now being joined by optical surface sensing, which can measure the body surface throughout treatment. Fourier profilometry has dynamic surface reconstruction capability and provides a dense array of measured points without interpolation. In common with other surface sensing methods the effects of background illumination and skin texture need to be removed prior to surfaced reconstruction. Direct background measurement and subtraction is ideal for this, in a static environment. However, motion is challenging. This paper compares background subtraction and a new partial differential approach to processing structured light images for Fourier profilometry. Results are presented for an anthropomorphic test phantom and patient undergoing treatment. It is shown that in the static test case the differential approach produces surfaces comparable to the ideal case of background subtraction to better than 0.5mm with a reproducibility of 0.1mm under clinical conditions. Patient results show that the differential approach produces body reconstructions without motion artifacts in the presence of free breathing for data gathered at 25Hz, whereas background subtraction fails. Copyright © 2011 ACTA Press.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".