Use of SPECT-CT for specific dose calculations based on accurate quantitative measurements of activity distribution
Bibliographic record
Abstract
529 Objectives: Determination of patient specific dose for optimized radiotherapy requires accurate quantitation of activity distribution. We used SPECT-CT data to determine the levels of quantitative accuracy achievable for Tc-99m, I-123, I-131 and In-111 after attenuation correction (AC), scatter correction (SC), and resolution recovery (RR). Methods: Two hot sources (32ml bottles) with identical activities were placed at different depths in the thorax phantom (Data Spectrum Corp.) filled with cold and active water. The experimental data were acquired on an integrated SPECT-CT (Infinia-Hawkeye-4 and Infinia-Hawkeye, GE Healthcare) according to a clinical protocol (99mTc-MDP bone scan, 131/123I-MIBG, and 111In-Octreoscan). For I-123 and I-131 data, a second energy window was acquired for high energy cross-talk and collimator septal penetration correction (SPC). Images were reconstructed with OSEM using 4 iterations and 10 subsets. Data reconstructions with 2D-RR, 3D-RR, 3D-RR+AC, 3D-RR+AC+SPC and 3D-RR+AC+SPC+SC were performed using our qSPECT code, and were compared to partially corrected GE reconstructions (i.e. w/w-o AC). In this preliminary analysis the relative difference between the numbers of counts in each bottle was used as a measure of quantitative accuracy of the reconstruction. Results: Fully-corrected reconstructions showed much improved quantitation. In one example study, the relative errors decreased from 95%, 33%, 23% and 10% when no corrections were applied to 6%, 12%, 11% and 8% with the comprehensive set of corrections for Tc-99m, I-123, In-111 and I-131, respectively. Conclusions: For quantitation of activity distribution, the best level of accuracy was achieved when all corrections were applied. Accordingly, SPECT-CT opens promising avenues for patient specific dosimetry. The analysis of relative importance of corrections and absolute quantitation are currently being performed.
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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 | 0.003 | 0.004 |
| 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 | 0.000 |
| 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".