FPGA based Time-To-Digital Converter for Time of Flight Computed Tomography
Bibliographic record
Abstract
Time-of-Flight Computed Tomography (ToF-CT) is a novel research path to improve current CT scanners. By measuring the travel time for photons to reach the detectors and discriminating scattered photons from primary photons, we expect to be able to reduce the dose delivered during the procedure or to increase the image quality. Many challenging requirements must be met in order to build a first ToF-CT prototype scanner. One of them is the ability to digitize the energy and time information of the incoming X-ray photons with a high precision and managing the high data rate.This work focuses on a Time-to-Digital Converter (TDC) built in an FPGA to measure the time of flight of X-ray photons. These time measurements, combined with the energy measurement of the incoming photons, are used to generate ToF histograms for different energy ranges. The current implementation can measure energy with a Time-Over-Threshold scheme using 1 ns time resolution, and can measure time-of-flight with 10 ps FWHM precision in a 2 ns narrow dynamic range and a 16 ps FWHM precision on its full scale range of around 30 ns. Moreover, the TDC is capable of sampling all hits at a 25 Mhit/s rate, which is sufficient for current ToF-CT goals. This TDC is scalable for a multi-channel implementation for the first ToF-CT scanner.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
FPGA time-to-digital converter for time-of-flight CT; instrumentation engineering.
This is an engineering study of a time-to-digital converter for medical imaging, not of research practice.
Domain medical-imaging hardware engineering; object is a TDC device, not research practice.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".