Application of voxelwise analysis in the detection of regions of reduced fractional anisotropy in multiple sclerosis patients
Bibliographic record
Abstract
PURPOSE: To investigate the utility of voxelwise analysis in the detection of lesions in the normal appearing white matter (NAWM) of individual multiple sclerosis (MS) patients. MATERIALS AND METHODS: Diffusion tensor imaging (DTI) was performed on 10 normal controls and six patients with MS lesions. The fractional anisotropy (FA) maps derived from the diffusion-weighted images were then spatially normalized (via an affine transformation) into Montreal Neurological Institute (MNI) space, and the normalized FA map of each of the patients was compared voxelwise with the normalized FA maps of the group of normals in a one-sample t-test (P = 0.0001). Two independent board-certified neuroradiologists reviewed the data. RESULTS: In the patient data for all six cases, the two reviewers determined detection sensitivities of 72% and 96% for the voxelwise technique based on known fluid-attenuated inversion-recovery (FLAIR) lesions. In addition, between the two reviewers, nine NAWM regions exhibiting FA reductions were identified in the six patients. However, numerous regions of abnormal FA were detected that were attributed to poor intersubject image registration. CONCLUSION: Voxelwise analysis of spatially normalized FA maps has the potential to identify regions of FA reduction in lesions and in the NAWM of individual MS patients in a rapid and reproducible fashion. J. Magn. Reson. Imaging 2007;26:552-556. (c) 2007 Wiley-Liss, Inc.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".