Design and Evaluation of a 2.5D-Assembled High-Density Detection Module for the Ultra-High Resolution Brain PET Scanner
Bibliographic record
Abstract
The LabPET II modular technology was identified as a promising platform for a new generation of Ultra-High Resolution (UHR) brain PET scanners. Scaling up from preclinical systems to achieve a human brain field-of-view introduces significant challenges that require high-density, low-noise detection modules capable of dissipating over 1.1 kW of heat within a confined volume. To address these constraints, a new UHR detection module, referred to as the UHR-DM, was developed based on a 2.5D stacked PCB architecture. This design embeds two readout ASICs between two circuit boards to enhance thermal management, mechanical alignment, and electrical performance, while preserving full backward compatibility with the existing LabPET II platform. This paper presents a detailed comparison between the UHR-DM and the original LabPET II detection module (LP2-DM). Key improvements include improved heat extraction through direct conduction, reduced parasitic capacitance via shorter and more uniform trace routing, and enhanced noise immunity enabled by embedded shielding. A dedicated testbench was used to evaluate thermal performance, baseline stability, noise characteristics, intrinsic and full-system coincidence time resolution (CTR), and time-over-threshold (ToT) energy resolution. Results show that the UHR-DM design reduces ASIC operating temperature by up to 43%, lowers noise by 60% when using high electronic gains, and improves channel-to-channel noise uniformity. It also achieves more consistent CTR and energy resolution without compromising any performance aspect of the original design. These enhancements will facilitate the upcoming integration all 129 024 individual channels required by the UHR brain PET scanner to achieve a spatial resolution of 1.25 mm at the center of the field of view.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".