Design of a high-resolution liquid xenon detector for positron emission tomography
Bibliographic record
Abstract
Positron Emission Tomography (PET) is a vital imaging technique extensively used for early cancer detection by visualizing metabolic processes in the body. While traditional PET systems use scintillation crystals like bismuth germanate (BGO) or lutetium oxyorthosilicate (LSO) to detect gamma rays, they have inherent energy and spatial resolution limitations. This paper proposes an advanced PET design using liquid xenon (LXe)-based detectors that integrate scintillation and ionization energy detection. Our PET detector design has a monolithic liquid xenon target of 5×5×5 cm3 , from where scintillation light is detected by silicon photomultipliers (SiPMs) placed on one side of the target. The ionization is converted to field-enhanced electroluminescence in liquid xenon and detected by the same SiPMs. We use Monte Carlo simulations to optimize the configuration of the electric field and improve the light collection efficiency. Combining both detection modes, the proposed system aims to significantly improve the energy resolution to approximately 2% full width at half maximum (FWHM). Furthermore, machine learning models enhance position reconstruction accuracy with sub-millimeter horizontal and depth-of-interaction (DOI) resolutions. The results indicate that the LXe-based PET detector can achieve superior performance compared to current PET technologies, offering enhanced imaging accuracy with the potential for reduced doses of radioactive tracer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".