SU‐E‐I‐170: Improvement of Quantification for Tumors with Non‐Uniform Activity Distributions: SPECT Simulation Study
Bibliographic record
Abstract
Purpose: We examined the ability of a template‐based method for partial volume effect correction (PVEC) to improve activity distributions inside tumors. Methods: We simulated oncology SPECT scans with an 80mL cylindrical object representing the tumor. This object was divided into two parts filled with different activities. In total, three cases with different activity ratios in those parts were considered and, correspondingly, the spill‐ in and spill‐out between (i) tumor/background and (ii) parts of the tumor were modeled. We assumed that a structural imaging modality could be used to visualize the boundaries of our tumor‐like object, but not the internal boundary between the parts of the object. From simulated SPECT acquisitions, we first reconstructed images by method M1 with corrections for attenuation, scatter, and resolution loss, portraying the most advanced reconstruction currently available in clinics. Secondly, we applied our PVEC technique M2, which iteratively updates activity both inside and outside the delineated tumor. This method takes into account that not only is the tumor affected by spill‐out, but also the surrounding background is affected by spill‐in. Results: Our PVEC effectively corrected for the partial volume effect by accurately modeling spill‐in and spill‐out through known “external” boundaries of the tumor‐like object. In three considered cases, method M1 underestimated total activities in tumors by 5–21%. After applying PVEC, these errors ranged from 8% to 12%. Additionally, method M2 improved the activity distribution. The relative error of the voxelized tumor activity distribution was 21–26% for method M1 and 11–17% for method M2. However, as the borderline between tumor parts was unavailable, accurate restoration of activity in this zone could not be performed. This limitation becomes more pronounced as the ratio of activities in these two parts increases. Conclusions: Template‐based PVECs can considerably improve not only the total activity, but also vozelized activity distributions in tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".