Improvement of in Vivo Quantification of [ <sup>123</sup> I]-Iodobenzovesamicol in Single-Photon Emission Computed Tomography/Computed Tomography Using Anatomic Image to Brain Atlas Nonrigid Registration
Bibliographic record
Abstract
Brain anatomy variability is a major problem in quantifying functional images in nuclear medicine, in particular relative to aging and neurodegenerative diseases. The aim of this study was to compare affine and elastic model-based methods for magnetic resonance imaging (MRI) to brain atlas registration and to assess their impact on the quantification of cholinergic neurotransmission. Patients with multiple system atrophy (MSA) and age-matched healthy subjects underwent an MRI and a single-photon emission computed tomographic (SPECT) examination using [123I]-iodobenzovesamicol (IBVM). Both affine and elastic methods were compared to register the subjects' MRI with the Montreal Neurological Institute brain atlas. Performance of the registration accuracy was quantitatively assessed and the impact on the IBVM quantification was studied. For both subject groups, elastic registration achieved better quantitative performance compared to the affine model. For patients suffering from neurogenerative disease, this study demonstrates the importance and relevance of MRI to atlas registration in quantification of neuronal integrity. In this context, in comparison with rigid registrations, an elastic model-based registration provides the best relocation of the brain structures to the atlas for accurately quantifying cholinergic neurotransmission.
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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 | 0.007 |
| 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 | 0.001 |
| 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".