Quantitative Measurement of In Vivo Tracer Concentration in Rats with Multiplexed Multi-Pinhole SPECT
Bibliographic record
Abstract
The goal of this study is to evaluate the quantitative accuracy of measuring in vivo tracer concentrations using multiplexed multi-pinhole microSPECT with CT-based attenuation correction (AC) and simple methods for scatter compensation. Phantom and in vivo rat cardiac images were acquired and reconstructed with no photon AC, with AC only, with attenuation and dual-energy window scatter correction, and using a reduced attenuation coefficient to compensate for scatter. Absolute calibration was also acquired using small sources to minimize self-attenuation and scatter. The phantom tracer concentrations measured with SPECT were compared to dose calibrator measurements. The rats were sacrificed and the cardiac activity measured in vivo was compared to well counter measurements of cardiac activity. With no correction, the tracer concentrations measured in phantoms was as much as 30% below the true value for the largest phantom (52 mm diameter). AC improved the accuracy of quantification, but overestimated activity concentrations by up to 5% for the larger phantoms. With DEW scatter correction, activity concentration measured with SPECT agrees with the dose calibrator measurements to within -2 ±2% and with a reduced attenuation coefficient, the agreement was better than -1 ±1%. In rats injected with99mTc-tetrofosmin, SPECT measurements of total cardiac activity were 24±2% below well counter measurements with no corrections, 5±3% above well counter measurements with only attenuation correction, 3±3% below with DEW scatter correction, and 1±3% below with reduced attenuation correction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".