Design Considerations for Mixed-Signal ASIC Readout in Time-of-Flight Computed Tomography
Bibliographic record
Abstract
Time-of-Flight Computed Tomography (ToF-CT) relies on high-speed digital processing to extract precise timing information, which is then used in image reconstruction. In typical configurations, TOF-CT generates raw data at rates exceeding$100 \text{Gbit} / \mathrm{s}$per$\text{mm}^{2}$, creating significant challenges in managing this large data volume. Additionally, the processing must fit within the limited silicon area of a densely packed, multi-channel ASIC. To address these issues, this paper explores adaptive binning along with asynchronous counters to design a histogram circuit for implementing low-area, real-time data compression for TOF-CT applications. We evaluated the trade-offs between compression efficiency and essential data needed to maintain image quality. A case study shows a reduction by a factor of six in data size and silicon area compared to a standard histogramming. The embedded signal processing has been tested with GATE simulations data and demonstrate a minimal impact on image quality, resulting in a$0.2 \backslash \%$loss in CNR.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".