The use of CT density changes at internal tissue interfaces to correlate internal organ motion with an external surrogate
Bibliographic record
Abstract
The purpose of this paper is to describe a non-invasive method to monitor the motion of internal organs affected by respiration without using external markers or spirometry, to test the correlation with external markers, and to calculate any time shift between the datasets. Ten lung cancer patients were CT scanned with a GE LightSpeed Plus 4-Slice CT scanner operating in a ciné mode. We retrospectively reconstructed the raw CT data to obtain consecutive 0.5 s reconstructions at 0.1 s intervals to increase image sampling. We defined regions of interest containing tissue interfaces, including tumour/lung interfaces that move due to breathing on multiple axial slices and measured the mean CT number versus respiratory phase. Tumour motion was directly correlated with external marker motion, acquired simultaneously, using the sample coefficient of determination, r(2). Only three of the ten patients showed correlation higher than r(2) = 0.80 between tumour motion and external marker position. However, after taking into account time shifts (ranging between 0 s and 0.4 s) between the two data sets, all ten patients showed correlation better than r(2) = 0.8. This non-invasive method for monitoring the motion of internal organs is an effective tool that can assess the use of external markers for 4D-CT imaging and respiratory-gated radiotherapy on a patient-specific basis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".