Design Considerations for Mixed-Signal ASIC Readout in Time-of-Flight Computed Tomography
Why this work is in the frame
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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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100 \text{Gbit} / \mathrm{s}$</tex> per <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mm}^{2}$</tex>, 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2 \backslash \%$</tex> loss in CNR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it