SU-FF-J-115: Quantification of Uptake Volumes in PET Images for Treatment Response Monitoring: Challenges and Solutions
Bibliographic record
Abstract
Purpose: Monitoring treatment response in PET images by quantifying changes in the spatial distribution of radioactivity concentration over time requires accurate determination of volumes. Thresholding methods are usually based on finding volume-recovering thresholds for known objects, such as spheres, in a discrete voxel grid. Measuring these volumes introduces discretization errors into the threshold calibration process. In addition, thresholds depend strongly on the object-to-background activity ratio which is usually unknown a-priori. In this work, the efficacy of a simple pre-processing step is investigated that makes the thresholds largely independent of the object-to-background activity ratio. Methods: PET scans of a standard imaging phantom containing 6 spheres were acquired in 3D mode on a GE Discovery ST scanner for different object-to-background activity ratios including no background. Images were reconstructed using FORE-OSEM with a variable number of iterations. Thresholds in images where the background activity was subtracted were compared to the thresholds in the no-background images. Discretization errors were quantified by placing mathematically accurate contours of the spheres into the voxel grid of the PET dataset. Volumes were computed by the treatment planning software. Results: Using a low number of iterations, volumes of all spheres were accurate to within 10% using thresholds derived from the background-corrected images. A higher number of iterations degraded the accuracy significantly. In coarse voxel grids common in PET imaging (2.3 × 2.3 × 3.3 mm), small volumes were consistently overestimated by as much as 30% depending on the position of the spheres with respect to the grid. Conclusions: Threshold calibration is sensitive to voxel grid resolution. For images acquired in 3D mode and reconstructed with a low number of iterations, a simple background correction method allows for accurate volume quantification independently of initial background activity and allows for a significant simplification of the threshold calibration process.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".