Quantitative SPECT/CT reconstruction for<sup>177</sup>Lu and<sup>177</sup>Lu/<sup>90</sup>Y targeted radionuclide therapies
Bibliographic record
Abstract
We investigated the quantitative accuracy of SPECT/CT imaging studies as would be performed before and after targeted radionuclide therapy (TRT) using phantom experiments with (i) (99m)Tc, (ii) ¹⁷⁷Lu and (iii) ⁹⁰Y/¹⁷⁷Lu. While the experiment with (99m)Tc imitated a diagnostic scan, the experiments with ¹⁷⁷Lu and ⁹⁰Y/¹⁷⁷Lu modeled post-therapy acquisitions. At the next stage, we reconstructed images from pre- and post-therapy patient studies. The data were first reconstructed using two methods with limited corrections for the physics effects. Then, to generate quantitatively accurate absolute activity distributions, we applied a hybrid (model-based and window-based) reconstruction strategy where some of the physics effects were accurately modeled while corrections for other effects were empirical and based on information obtained from the projection data. The accuracies of absolute activity recovered by the hybrid method from the six phantom experiments were very similar to each other and acceptable for potential use in TRT. When measured in identical regions of interest, the (99m)Tc ⁹⁰activity was reconstructed with errors ranging between -3.3% and 2.9%, while the ¹⁷⁷Lu activity was reconstructed from experiments with ¹⁷⁷Lu and Y/¹⁷⁷Lu with errors ranging between -1.6% and 1.6%. The reconstruction algorithms with limited corrections led to larger and case-specific errors as might have been expected. From a clinical prospective, our results showed that physics-based reconstructions improved resolution of images corresponding to both diagnostic scans with (99m)Tc and post-therapy scans with ¹⁷⁷Lu. Our analysis of patient study demonstrated that lack of corrections led to overestimation of activities in organs and tumor by 29-39% for the diagnostic scan with (99m)Tc and by 105-218% for post-therapy scan with ¹⁷⁷Lu.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".