Dual time-point quantitative SPECT-CT parathyroid imaging using a single computed tomography
Bibliographic record
Abstract
OBJECTIVES: Dual-phase parathyroid scan with Tc-sestamibi is a standardized imaging method for diagnosing parathyroid adenoma and hyperplasia. Conventional planar images using a gamma camera are performed routinely in early and delayed time points, followed by a single SPECT-CT. SPECT-CT on both early and delayed time points, although clinically useful, is not commonly performed to avoid extra radiation exposure from computed tomography (CT). This study explores the feasibility of co-registering early and delayed SPECT-CT from a single CT and evaluates the most effective combination of images for co-registration. PATIENTS AND METHODS: Fourteen retrospective patients with early and delayed planar and SPECT-CT images were recruited for this validation study. Results from contemporaneous early and delayed SPECT-CT, with hardware matched registration, are considered the gold standard. Noncontemporaneous early SPECT with delayed CT and vice versa were also processed with manual alignment by an experienced and a novice operator three times each to evaluate interoperator and intraoperator variability. Maximum standardized uptake values (SUVmax) of the thyroid lobes and parathyroid adenomas were measured, and the results in terms of accuracy and precision from noncontemporaneous SPECT-CT acquisitions were evaluated. RESULTS: Good image quality from co-registered SPECT-CT acquired at different time points with the results showed no bias (P>0.5). The co-registration of early SPECT and delayed CT showed higher precision than the alternative combination. Overall, the experienced operator achieved better precision and intraoperator variability than the novice operator (reproducibility coefficient=33% SUV vs. reproducibility coefficient=54% SUV, P<0.001). CONCLUSION: Quantitative SUV measurement from early and delayed parathyroid SPECT-CT imaging is feasible, with the best result achieved by experienced operators using delayed CT in manual registration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".