Quantitative SPECT imaging of 155Tb and 161Tb for preclinical theranostic radiopharmaceutical development
Bibliographic record
Abstract
Abstract Background Element-equivalent matched theranostic pairs facilitate quantitative in vivo imaging to establish pharmacokinetics and dosimetry estimates in the development of preclinical radiopharmaceuticals. Terbium radionuclides have significant potential as matched theranostic pairs for multipurpose applications in nuclear medicine. In particular, 155Tb (t1/2 = 5.32 d) and 161Tb (t1/2 = 6.89 d) have been proposed as a theranostic pair for their respective applications in single photon emission computed tomography (SPECT) imaging and targeted beta therapy. Our study assessed the performance of preclinical quantitative SPECT imaging with 155Tb and 161Tb. A hot rod resolution phantom with rod diameters ranging between 0.85 and 1.70 mm was filled with either 155Tb (21.8 ± 1.7 MBq/mL) or 161Tb (23.6 ± 1.9 MBq/mL) and scanned with the VECTor preclinical SPECT/CT scanner. Image performance was evaluated with two collimators: a high energy ultra high resolution (HEUHR) collimator and an extra ultra high sensitivity (UHS) collimator. SPECT images were reconstructed from photopeaks at 43.0 keV, 86.6 keV, and 105.3 keV for 155Tb and 48.9 keV and 74.6 keV for 161Tb. Quantitative SPECT images of the resolution phantoms were analyzed to report inter-rod contrast, recovery coefficients, and contrast-to-noise metrics. Results Quantitative SPECT images of the resolution phantom established that the HEUHR collimator resolved all rods for 155Tb and 161Tb, and the UHS collimator resolved rods ≥ 1.10 mm for 161Tb and ≥ 1.30 mm for 155Tb. The HEUHR collimator maintained better quantitative accuracy than the UHS collimator with recovery coefficients up to 92%. Contrast-to-noise metrics were also superior with the HEUHR collimator. Conclusions Both 155Tb and 161Tb demonstrated potential for applications in preclinical quantitative SPECT imaging. The high-resolution collimator achieves < 0.85 mm resolution and maintains quantitative accuracy in small volumes which is advantageous for assessing sub organ activity distributions in small animals. This imaging method can provide critical quantitative information for assessing and optimizing preclinical Tb-radiopharmaceuticals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".