Implementation of the Wobbling Technique with Spatial Resolution Enhancement Approach in the Xtrim-PET Preclinical Scanner: Monte Carlo Simulation and Performance Evaluation
Bibliographic record
Abstract
Purpose: This study aims to develop and implement a wobbling data acquisition mode in the Xtrim-PET scanner to enhance spatial resolution in preclinical Positron Emission Tomography (PET) imaging. Materials and Methods: To evaluate the performance of the Xtrim-PET scanner with the wobbling motion, simulations were conducted using the Gate Monte Carlo toolkit. The positions of all detected Lines Of Responses (LORs) were adjusted based on the magnitude of the wobbling movement to minimize image blurring. Different stop point configurations ranging from 4 to 256 were investigated to optimize the number of wobbling points. The performance of the wobbling data acquisition mode was assessed using IQ NEMA-NU4 and Hot-Rod phantoms, as well as phantoms resembling mice and rats. Two reconstruction methods were employed to assess image quality: Filtered Back-Projection (FBP) with various filters and the iterative method, OSEM, with 5 and 10 iterations. Results: The results from NEMA tests using Monte Carlo simulations closely matched experimental measurements, demonstrating the accuracy of the simulations. Based on sinograms obtained from the uniform cylinder phantom scan and considering the constraints associated with the mechanical movement system, it was decided to use 4 stopping points for the wobbling movement. The implementation of the wobbling technique resulted in a spatial resolution of 0.91 mm at the center of the scanner, while without the technique, the resolution was 1.93 mm. The wobbling motion did not significantly affect sensitivity, NECR, or SF values. However, it notably improved spatial resolution, especially with the OSEM method, enhancing image quality by up to 52.8%. Conclusion: The wobbling technique offers a substantial enhancement in spatial resolution for preclinical PET scanners. Although achieving sub-micrometer spatial resolutions theoretically seems feasible by increasing the number of stopping points, practical limitations present challenges. Nonetheless, the wobbling
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".