A New Method for Assessing Tissue Alignment using Clinical MRI in Multiple Sclerosis
Bibliographic record
Abstract
Changes in the alignment of white matter tracts are common in many neurological disorders such as multiple sclerosis (MS). Currently advanced MRI methods including diffusion-weighted imaging is the mainstay in assessing tissue coherency and anisotropy. In this thesis, I have implemented and verified a novel image-processing method for this purpose using conventional MRI. This is done based on Fourier transform power spectrum. Outcomes were evaluated in 3 steps: 1) testing feasibility using brain areas with highly aligned nerve fiber tracks in T2- weighted MRI; 2) confirming pathological relevance using postmortem brain sample; and 3) assessing utility by comparing with diffusion tensor imaging. To improve the accuracy of comparison with pathology, I have also conducted quantitative histology besides traditional analysis of the staining density of myelin and axons. The results suggest that advanced analysis of clinical MRI may provide valuable information as powerful as advanced MRI to enhance the measurement of tissue property.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 0.001 |
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".