Quantitative phase and texture angularity analysis of brain white matter lesions in multiple sclerosis
Bibliographic record
Abstract
Structural changes to nerve fiber tracts are extremely common in neurological diseases such as multiple sclerosis (MS). Accurate quantification is vital. However, while nerve fiber damage is often seen as multi-focal lesions in magnetic resonance imaging (MRI), measurement through visual perception is limited. Our goal was to characterize the texture pattern of the lesions in MRI and determine how texture orientation metrics relate to lesion structure using two new methods: phase congruency and multi-resolution spatial-frequency analysis. The former aims to optimize the detection of the ‘edges and corners’ of a structure, and the latter evaluates both the radial and angular distributions of image texture associated with the various forming scales of a structure. The radial texture spectra were previously confirmed to measure the severity of nerve fiber damage, and were thus included for validation. All measures were also done in the control brain white matter for comparison. Using clinical images of MS patients, we found that both phase congruency and weighted mean phase detected invisible lesion patterns and were significantly greater in lesions, suggesting higher structure complexity, than the control tissue. Similarly, multi-angular spatial-frequency analysis detected much higher texture across the whole frequency spectrum in lesions than the control areas. Such angular complexity was consistent with findings from radial texture. Analysis of the phase and texture alignment may prove to be a useful new approach for assessing invisible changes in lesions using clinical MRI and thereby lead to improved management of patients with MS and similar disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".