Time-of-flight computed tomography - proof of principle
Bibliographic record
Abstract
Computed tomography has greatly improved over the last decade, especially through x-ray dose exposure reduction while maintaining image quality. Herein, a new concept is proposed to improve the contrast-to-noise ratio (CNR) by including the time-of-flight (TOF) information of individual photons to obtain further insight on the photon's trajectory and to reject scatter contribution. The proof of the concept relies on both simulation and experimental measurements in a cone-beam computed tomography arrangement. Results show a statistical difference between the TOF of scattered and primary photons exploitable in TOF computed tomography. For a large volume of the size of a human abdomen, a scatter reduction from 296% to 4% is achieved in our simulation setup with perfect timing measurements which yields a 110% better CNR, or a dose reduction by a factor of four. Cup artifacts are also reduced from 24.7% to 0.8%, and attenuation inaccuracies are improved from -26.3% to -0.8%. With 100 ps and 10 ps FWHM timing jitters, respectively 75% and 95% of the scatter contribution can be removed with marginal gains below 10 ps. Experimental measurements confirm the feasibility of measuring statistical differences between the TOF of scattered and primary photons.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".