Exploring the Feasibility of Implementation of a Whole TOF-CT System with Current Technology
Bibliographic record
Abstract
Recently, Time-of-Flight (ToF) X-ray imaging gained attention as an approach to negate the adverse effects of scattered photons as well as increase the CNR of the images. The principle is based on the analysis of each individual photon to identify and eliminate those with a longer time of flight than expected for ballistic photons. This technology is based on a pulsed X-ray source synchronized with an array of time-accurate detectors. Currently, Université de Sherbrooke is developing this new technology and aims to design the first scanner able to produce ToF radiography and CT images. The next step toward clinical use of this technology is the design of a fan-beam scanner based on 100 mm2detector circuits distributed on a rotating ring. We are designing a new ASIC to integrate all the front-end electronics for this scanner which is able to timestamp X-ray photons with better than 300~ps~FWHM resolution. In this paper, the first digital architecture based on time histogramming and distributed signal processing in ASICs and FPGAs is proposed. Also, a model based on the aforementioned specifications is simulated to further study the requirements for the front-end electronics. These simulations, along with the physical constraints, provide insights into validating the proposed digital architecture and estimating the data rates at different stages of the signal processing and system. These estimations and calculations showed a good agreement with the feasibility of implementing time-of-flight computed tomography with existing technology.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".