Dual SPECT imaging of <sup>111</sup>In and <sup>67</sup>Ga to simultaneously determine <i>in vivo</i> the pharmacokinetics of different radiopharmaceuticals: a quantitative tool in pre-clinical research
Bibliographic record
Abstract
Abstract Dual-isotope (DI) studies offer a number of advantages in pre-clinical imaging. These include: reducing study times when compared with sequential scans, reducing the number of animals required for any given study, and most importantly, producing images perfectly registered in space and time that provide simultaneous information about two distinct body functions. The ability of single photon emission computed tomography (SPECT) to measure and differentiate energies of the emitted photons makes it well suited for DI imaging. However, since scattered photons originating from one radioisotope may be detected in the energy window of the other and thus degrade image quality and quantitative accuracy, scatter and crosstalk corrections must be applied. The decay characteristics of 111 In and 67 Ga, which are suitable for quantitative DI imaging for up to 2 weeks post-injection, led us to investigate the performance of simultaneous 111 In/ 67 Ga SPECT imaging using a small-animal pre-clinical scanner. A series of phantom experiments were performed to investigate image quality and accuracy of activity quantification in 111 In/ 67 Ga images acquired with three different collimators and reconstructed from different photopeak combinations. The triple energy window (TEW) method was used to correct for scatter and crosstalk. Based on these phantom studies, the optimal selection of collimator and energy window settings was determined. When using these optimal settings, submillimeter-size structures were distinguishable in the reconstructed images and quantification errors below 20% were achieved for both isotopes. The optimal parameters were subsequently applied to an in vivo animal study. The determination of the distinct pharmacokinetic profiles of two polymer radiopharmaceuticals injected simultaneously, but by different administration routes (intravenously and intraperitoneally) into a single animal demonstrated the feasibility of simultaneous 111 In/ 67 Ga SPECT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".