<title>Blind deconvolution of human brain SPECT images using a distribution mixture estimation</title>
Bibliographic record
Abstract
Thanks to its ability to yield functionally-based information, the SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, SPECT images are blurred and consequently their interpretation by the clinician is often difficult. In order to improve the spatial resolution of these images and then to facilitate their interpretation, we propose herein to implement a deconvolution procedure relying on an accurate distribution mixture parameter estimation procedure. Parameters of this distribution mixture are efficiently exploited in order to prevent overfitting of the noisy data or to determine the support of the object to be deconvolved when this one is needed. In this context, we compare the deconvolution results obtained by the Lucy-Richardson method and by the recent blind deconvolution technique called the NAS-RIF algorithm on real and simulated brain SPECT images. The NAS-RIF performs the best and shows significant contrast enhancement with little mottle (noise) amplification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".