Evaluation of linear registration algorithms for brain SPECT and the errors due to hypoperfusion lesions
Bibliographic record
Abstract
The semiquantitative analysis of perfusion single-photon emission computed tomography (SPECT) images requires a reproducible, objective method. Automated spatial standardization (registration) of images is a prerequisite to this goal. A source of registration error is the presence of hypoperfusion defects, which was evaluated in this study with simulated lesions. The brain perfusion images measured by 99mTc-HMPAO SPECT from 21 patients with probable Alzheimer's disease and 35 control subjects were retrospectively analyzed. An automatic segmentation method was developed to remove external activity. Three registration methods, robust least squares, normalized mutual information (NMI), and count difference were implemented and the effects of simulated defects were compared. The tested registration methods required segmentation of the cerebrum from external activity, and the automatic and manual methods differed by a three-dimensional displacement of 1.4+/-1.1 mm. NMI registration proved to be least adversely effected by simulated defects with 3 mm average displacement caused by severe defects. The error in quantifying the patient-template parietal ratio due to misregistration was 2.0% for large defects (70% hypoperfusion) and 0.5% for smaller defects (85% hypoperfusion).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".