Reduced Time-Of-Flight Measurements for Accurate Scatter Estimation and Correction X-Ray Ct
Bibliographic record
Abstract
Time-of-flight computed tomography is proposed as a solution to remove scattered photons with minimal loss in sensitivity as compared to antiscatter grids. Scattered photons are discriminated by measuring the time-of-flight of each photon with a synchronized pulsed X-ray source and a time-resolved detector. However, existing pulsed X-ray sources and scintillator-based photon counting detectors have a limited event rate lead to long acquisition times. To circumvent this problem, this paper explores, in simulation, the use of time-of-flight measurements with reduced photon counts to estimate the scatter and correct a regular photon counting computed tomography scan in the absence of an antiscatter grid. Simulation results show that time-of-flight scatter estimation with a 200 ps FWHM timing resolution with as few as 1000 counts/pixel allows to accurately correct for scatter in a photon counting computed tomography projection with a 4.6% mean absolute percent error. Results also show that improving the timing resolution and increasing the number of counts/pixel leads to an increasingly better estimation.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".