TOF Benefits and Trade-offs on Image Contrast-to-Noise Ratio Performance for a Small Animal PET Scanner
Bibliographic record
Abstract
Recently, time-of-flight (TOF) scanners have become a mainstream in positron emission tomography research particularly owing to their ability to improve the image contrast-to-noise ratio (CNR). As the scintillation photon transport directly affects the coincidence time resolution, decreasing crystal length can be considered to improve timing performance even at the cost of sensitivity loss. This would also improve the radial spatial resolution and reduce the cost of the scintillator material, particularly in clinical scanners. Hence, this article investigates the tradeoffs between TOF, crystal length, and scan time with the goal of using TOF to compensate for CNR degradation caused by decreasing the scintillator volume in a highly pixelated scanner. To do this, a TOF model of the LabPET II small animal scanner was developed. The contrast recovery coefficient (CRC) and CNR performance were investigated through a factorial design. This was followed by assessing when TOF gain may be advantageous in small animal imaging. Results show that decreasing crystal length by 2 mm improves CRC performance for such a scanner while CNR can be fully recovered by increasing scan time. It was also observed that the same CNR can be reached for a shorter acquisition time if faster TOF resolution is achieved. This article concludes with a summary of the trade offs to optimize the 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".