Experimental verification of 3D detector response compensation using the OSEM reconstruction method
Bibliographic record
Abstract
Detector blurring is a major factor in SPECT image degradation and several methods to include detector response compensation (DRC) into iterative reconstruction algorithms have been proposed. Here, we present a study to validate and quantify the performance of such correction using an approach that we have developed and which combines 3D DRC with a non uniform attenuation correction. The objective of this study was to estimate what is the best spatial resolution that could be experimentally achieved in an image reconstructed with both attenuation and resolution recovery corrections. For this purpose we have performed several tests involving physical phantoms and computer simulations. In experiments with capillary tubes in air the resolution came close to the pixel size and was equal to 3.6 mm. DRC convergence was slower when background activity was present but still resulted in an excellent, close to the pixel size, resolution in the image. Similar results were obtained for extended sources scanned in air and with background activity. In this last case, however, the speed of convergence of the reconstruction algorithm was substantially decreased. Additionally, we present the results of a study involving a simulated wrist model which showed that hot and cold defects of the size equal to /spl ap/6 mm can be detected when DRC was used. In summary, our experiments and simulations have demonstrated that including 3D DRC in the iterative reconstruction algorithm resulted in great improvement of image resolution and noise reduction that increased visibility of small details.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".