A Cone-Shaped Phantom for Assessment of Small Animal PET Scatter Fraction and Count Rate Performance
Bibliographic record
Abstract
PURPOSE: Positron emission tomography (PET) image quality deteriorates as the object size increases owing to increased detection of scattered and random events. The characterization of the scatter component in small animal PET imaging has received little attention owing to the small scatter fraction (SF) when imaging rodents. The purpose of this study is first to design and fabricate a cone-shaped phantom which can be used for measurement of object size-dependent SF and noise equivalent count rates (NECR), and second, to assess these parameters for two small animal PET scanners as function of radial offset, object size and lower energy threshold (LET). METHODS: The X-PET™ and LabPET-8™ scanners were modeled as realistically as possible using GATE Monte Carlo simulation platform. The simulation models were validated against experimental measurements in terms of sensitivity, SF and NECR. The dedicated phantom was fabricated in-house using high-density polyethylene. The optimized dimensions of the cone-shaped phantom are 158 mm (length), 20 mm (minimum diameter), 70 mm (maximum diameter) and taper angle of 9°. RESULTS: The relative difference between simulated and experimental results for the LabPET-8™ scanner varied between 0.7% and 10% except for a few results where it was below 16%. Depending on the radial offset from the center of the central axial field-of-view (3-6 cm diameter), the SF for the cone-shaped phantom varied from 26.3% to 18.2%, 18.6 to 13.1% and 10.1 to 7.6% for the X-PET™, whereas it varied from 34.4% to 26.9%, 19.1 to 17.0% and 9.1 to 7.3% for the LabPET-8™, for LETs of 250, 350 and 425 keV, respectively. The SF increases as the radial offset decreases, LET decreases and object size increases. The SF is higher for the LabPET-8™ compared with the X-PET™ scanner. The NECR increases as the radial offset increases and object size decreases. The maximum NECR was obtained at a LET of 350 keV for the LabPET-8™ and 250 keV for the X-PET™. High correlation coefficients for SF and NECR were observed between the cone-shaped phantom and an equivalent volume cylindrical phantom for the three considered axial fields of view. CONCLUSIONS: A single cone-shaped phantom enables the assessment of the impact of three factors, namely radial offset, LET and object size on PET SF and count rate estimates. This phantom is more realistic owing to the non-uniform shape of rodents' bodies compared to cylindrical uniform phantoms and seems to be well suited for evaluation of object size-dependent SF and NECR.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".