Sci—Thur PM: Imaging — 05: Calibration of a SPECT/CT camera for quantitative SPECT with <sup>99m</sup>Tc
Bibliographic record
Abstract
While quantitation is the norm in PET, it is not widely available yet in SPECT. This work's aim was to calibrate a commercially available SPECT/CT system to perform quantitative SPECT. Counting sensitivity, dead‐time (DT) constant and partial volume effect (PVE) of the system were assessed. A dual‐head Siemens SymbiaT6 SPECT/CT camera equipped with low energy high‐resolution collimators was studied. 99mTc was the radioisotope of interest because of its wide usage in nuclear medicine. First, point source acquisitions were performed (activity: 30–990MBq). Further acquisitions were then performed with a uniform Jaszczak phantom filled with water at high activity (25–5000MBq). PVE was studied using 6 hot spheres (diameters: 9.9–31.2 mm) filled with 99mTc (2.8MBq/cc) in the Jaszczak phantom, which was: (1) empty, (2) water‐filled and (3) water‐filled with low activity (0.1MBq/cc). The data was reconstructed with the Siemens's Flash3D iterative algorithm with 4 subsets and 8 iterations, attenuation‐correction (AC) and scatter‐correction (SC). DT modelling was based on the total spectrum counting rate. Sensitivity was assessed using AC‐SC reconstructed SPECT data. Sensitivity and DT for the sources were 99.51±1.46cps/MBq and 0.60±0.04µs. For the phantom, sensitivity and DT were 109.9±2.3cps/MBq and 0.62±0.13µs. The recovery‐coefficient varied from 5% for the 9.9mm, to 80% for the 31.2mm spheres. With our calibration methods, both sensitivity and DT constant of the SPECT camera had little dependence on the object geometry and attenuation. For small objects of known size, recovery‐coefficient can be applied to correct PVE. Clinical quantitative SPECT appears to be possible and has many potential applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.030 |
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".