YRT-PET-NX: Development of a Plugin for an Open-Source PET Reconstruction Platform to Support the NeuroEXPLORER
Bibliographic record
Abstract
Ultra-high resolution PET scanners offer multiple benefits to brain imaging for researchers and clinicians. The NeuroEXPLORER (NX) scanner, a brain-dedicated PET system with high resolution and high sensitivity, achieves exceptional image quality. These benefits, however, introduce challenges for image reconstruction in terms of speed, memory usage and need for accurate system modeling and motion correction. Open-source third-party image reconstruction tools can be a valuable substitute to manufacturer software to address these challenges, as well as to enable the evaluation of different modeling approaches and algorithms. In this work, the opensource PET reconstruction platform YRT-PET (Yale Reconstruction Toolkit for Positron Emission Tomography) is extended through a plugin to support the NX scanner. YRT-PET provides a scanner-agnostic reconstruction platform with research-oriented features. The YRT-PET-NX plugin integrates detector geometry, listmode packet decoding and scanner-specific corrections. We evaluated the performance by reconstructing a mini-Derenzo phantom, a non-human primate, and a human brain, comparing YRT-PET results with those from the vendor software and the MOLAR engine. Based on the mini-Derenzo reconstructions, comparable resolution was achieved, with a slight gain in peak-to-valley ratios. Across both non-human primate and human datasets, YRT-PET produced nearly-identical images. Time-activity curves for the non-human primate were quantitatively consistant, with a 5 - to 18 -fold reduction in reconstruction time due to GPU acceleration. These results demonstrate that the YRT-PET-NX plugin enables efficient and highquality reconstructions comparable to preexisting and established software. This work enables the features of YRT-PET alongside the high resolution of the NX. Future work aims to expand support for additional corrections such as randoms and scatter estimation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".