Implementation of an analytically based scatter correction in SPECT reconstructions
Bibliographic record
Abstract
Photon scattering is one of the main effects contributing to the degradation of image quality and to quantitative inaccuracy in nuclear imaging. We have developed a scatter correction based on the analytic photon distribution (APD) method, and implemented it in an iterative image reconstruction algorithm. The performance of the method was evaluated using computer simulated projection data, experimental data obtained from physical phantoms, and patient data. The scatter corrected images were compared to images that were only corrected for attenuation and collimator blurring. In the simulation studies our results could also be compared to an ideal scatter correction in which images were reconstructed only from unscattered photon data. In all cases, our scatter corrected images demonstrate improved image contrast. In the simulated data the contrast for images only corrected for attenuation and collimator blurring were on average 29% poorer than the ideal correction. Images for which our scatter correction was applied had contrast that was on average within 3% of the ideal correction. The scatter corrected reconstruction requires between 4 to 5 hours of total CPU time, on a 1.7 GHz processor with 1Gb RDRAM, using clinical data.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".