Clinical Evaluation of BSREM Reconstruction in Pediatric Oncology Using [ <sup>18</sup> F]FDG PET/CT
Bibliographic record
Abstract
Recent technologic advancements in PET, including silicon photomultipliers and block-sequential regularized expectation maximization (BSREM) tools, have allowed for renewed assessment of the optimal acquisition and reconstruction parameters in pediatric imaging. This work evaluates the performance of BSREM reconstruction and varied count density (CD) in digital [18F]FDG PET/CT to investigate the feasibility of reducing the injected activity or duration of acquisition in children with cancer. Methods: Five hundred unique reconstructions from 20 pediatric patients evaluated with PET/CT per clinical standard of care (SOC) were included in this retrospective study. Three-dimensional, whole-body imaging was acquired on a silicon photomultiplier PET/CT system in list mode with time-of-flight modeling. Imaging volumes were reconstructed with varying time per bed position (180, 120, 90, 60, and 45 s) to simulate a range of CDs using conventional iterative techniques (ordered-subset expectation maximization) and BSREM with varied regularization strength (β, 175–700). Two pediatric nuclear medicine physicians individually scored all studies, with patient information, reconstruction method, and CD concealed, rating technical quality and overall diagnostic satisfaction on a 5-point Likert scale. Quantitative SUV measurements on all reconstructions were compared with the clinical SOC. Results: Reconstruction with BSREM with a β of 500 or greater significantly improved overall scores across all CDs when compared with ordered-subset expectation maximization. Noise performance improved after application of a higher regularization parameter. Spatial resolution (sharpness) was greatest with a β of 350. Mean overall image quality at 25% CD using a β of 500 or greater was considered diagnostic. Mean liver and blood-pool SUV-to-noise ratio performed best with the highest β and CD. SUVmax behavior was complex, varying with reconstruction strength and CD, with measurements at β of 500 or greater differing from the SOC by no more than 15% across all CDs, and specific combinations varying by 10% or less. Conclusion: Clinical evaluation of whole-body [18F]FDG PET/CT in pediatric patients was diagnostic at all reductions in CD when using BSREM with a β of 500 or greater. Quantitative performance was variable, yet SUVmax differences of 10% or less were achievable with the appropriate selection of acquisition and reconstruction parameters. This study found that customized imaging parameters can reduce injected activity (radiation dose) and imaging time to best suit the pediatric patient.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".