Sci-Thurs PM: Planning-07: Impact of Quantitative SPECT Corrections on SPECT-Weighted Mean Dose and Functional Lung Volume Segmentation as Applied in Functional Sparing RT Planning
Bibliographic record
Abstract
Purpose: To compare functional lung volume segmentation and Single Photon Emission Computed Tomography (SPECT) weighted mean dose (SWMD) approaches used in SPECT guided treatment planning studies for patients with lung cancer. Methods and Materials: Nine lung cancer patients were consented to have a perfusion SPECT scan with 99mTc-macroaggregated albumin. Four image sets were reconstructed from each scan: one using a vendor provided ordered subsets expectation maximization (OSEM) algorithm and three quantitative SPECT reconstructions using OSEM methods with different types of attenuation and scatter corrections. SWMDs were calculated for open field with different sizes and gantry angles. Functional lung volumes were segmented in each reconstructed image using 10, 20, …, 90% of maximum SPECT intensity as a threshold. Results: Image reconstruction accuracy and thus functional lung volume segmentation are affected by several factors, such as attenuation and scatter correction, resolution recovery method and number of iterations used in reconstruction. Large differences in functional volumes (more than 50%) were found between images obtained from the four reconstructions. In contrast, the SWMD calculation produced consistent results for all SPECT reconstructions which included attenuation correction, regardless of whether or not scatter correction was used and the number of iterations. Conclusion: Functional volume segmentation is sensitive to the type of attenuation and scatter correction and number of iterations. In contrast, for images reconstructed with attenuation correction, the SWMD calculation produces consistent results and appears to be a more robust choice to be used in future studies incorporating SPECT into treatment planning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".