Automatic and Scalable SiPM Calibration for Multi-Channel Time-Of-Flight Radiography Instrumentation
Bibliographic record
Abstract
The SiPM has ignited the practical use of timeof-flight measurements in several applications. More recently, the technique has been proposed for radiography and computed tomography, where the timing information leads to the discrimination of ballistic and scattered photons. This translates into an improved contrast to noise ratio in the image without the use of antiscatter grid collimators. On the other hand, the SiPM operational conditions in these applications are very different from widespread ones such as Positron Emission Tomography, notably with a 5 to 10 times lower radiation energy. Calibration methods must therefore be adapted, but also scalable and with very short execution time in anticipation of future imaging systems with thousands of channels. This work proposes an automatic and distributed calibration methodology adapted to ToF in radiology and computed tomography. It is validated on a table-top 2 channel test setup, but with keeping an outlook on having a concept usable in a clinical context.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".