Artifact suppression and quantitative accuracy of damped MLEM algorithm for SPECT imaging
Bibliographic record
Abstract
The most disadvantageous artifacts that may accompany the maximum likelihood expectation maximization (MLEM) reconstruction are noise amplification and image deterioration near the edges of an area with sharp intensity change. In this paper, we investigate the performance of a damped MLEM (dMLEM) algorithm with a modified likelihood function [White R.L., J.SPIE Records, 2198, pp. 1342–1348, 1994] using simulated SPECT data. In particular, a trade-off between artifact suppression and quantitative accuracy of images is of our special interest. In our experiments, both noiseless and noisy numeric phantom data were created. Small cylindrical object with a 5cm diameter was located off-center in a large cylinder having a diameter of 16cm. Acquisition parameters were selected to model a typical SPECT clinical protocol. The images were reconstructed using both dMLEM (with two flattering parameters N=2 and N=3) and a conventional MLEM (corresponding to N=0) algorithm with up to 100 iterations. The modification of the likelihood function in dMLEM led to the suppression of both ringing and noise artifacts. This effect strongly depends on the damping parameter N. With increasing of N, the appearance of artifacts is delayed in the iteration process, but also the quantitative accuracy of images is degraded. Our simulations showed that slight damping of likelihood function in MLEM algorithm might serve as a reasonable tradeoff by allowing users for the suppression of artifacts while preserving the quantitative accuracy at an acceptable level. Especially, dMLEM method with N=2 allowed us to improve reconstruction convergence and avoid ringing artifacts in the first 20–30 iterations while quantitative accuracy of the recovered activity was degraded by only 3–4%.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".