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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".