A Real-Time Embedded Digital Processing Architecture for a Modular Time-of-Flight Computed Tomography Readout Using PETsys TOFPET 2C ASIC
Bibliographic record
Abstract
Time-of-flight (TOF) in computed tomography (CT) can lead to an increase in image quality with no loss of sensitivity. To implement this approach a modular 256 channels time-of-flight CT using four PETsys TOFPET 2C ASICs is actively being developed. One of the challenges with this system is the quantity of raw data needed to produce the image. On a full-scale CT scanner with the current TOF limitations, up to 120 Tb/s can be generated. To solve this problem, a Zynq-based system on module is used to deserialize the data coming from the four ASICs, do real-time correction and computation of TOF and generate one histogram per channel in a dedicated dual port RAM. With the help of a custom device driver, the histogram data is read directly from the memory and is sent through an Ethernet link to a monitoring PC. The histograms generation and transmission has been tested using simulation data and has been able to handle a data rate superior to the TOFPET 2C ASIC maximum with no dead time for reading the histograms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".