Quantification of the change in SUV due to the presence of defective detector blocks in clinical FDG PET imaging
Bibliographic record
Abstract
1507 Objectives Clinical PET systems based on block detector designs suffer occasional block detector failures, which can result in patient scan cancellations. In this study we examine the effects of defective detectors on measurements of SUVmax through imaging of a torso phantom. Methods A Data Spectrum anthropomorphic torso phantom was imaged in a normally functioning Siemens Biograph 16 HiRez PET/CT scanner using a whole-body imaging protocol. The torso volume had an activity concentration defined as a SUV of 1.0. Six spherical and shell lesions with different activity concentrations were placed in the phantom. 11 one-block and 3 two-block defect cases were created by zeroing lines of response in the sinograms. The images were reconstructed using the standard clinical OSEM protocol. SUVmax was measured for each lesion and defect case on a Siemens Leonardo Workstation, with the SUVmax for the normal case (i.e. no defect present) serving as the true value for comparison. Results Our preliminary results show that the average decrease in SUVmax over all lesions was 2% for one-block and 6% for the two-block defect cases. The largest observed change in SUVmax was 6% for a one-block defect and 8% for a two-block defect. Conclusions In the presence of one or two defective block detectors, the observed change in SUVmax was in no case large enough to affect the clinical diagnosis of a lesion. Thus for these cases we recommend proceeding with routine patient scanning, with the caveat of making the reading physicians aware of the defect being present. Research Support Winnipeg Health Sciences Foundation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".