Quantitative magnetic resonance imaging of magnetization transfer and T2 relaxation in human white matter pathology
Bibliographic record
Abstract
The primary aim of this thesis is the reconciliation of two seemingly disparate quantitative magnetic resonance imaging (MRI) techniques proposed to characterize human brain white matter (WM) in health and disease. Quantitative magnetization transfer imaging (QMTI) and multi-component analysis of T2 relaxation (QT2) both attempt to quantify myelin content in vivo, but are based on fundamentally different models of WM. QMTI probes the macromolecular component of tissue using a two-pool model of magnetization transfer, while QT2 isolates the water signal from distinct micro-anatomical compartments. The specific objectives were to determine the interrelationship between measurements made with both techniques in the context of potential pathological changes associated with multiple sclerosis (MS), and to apply both to track WM changes in the acute phase of MS lesions. First, simulations were used to evaluate the theoretical sensitivity of each technique to the characteristics of a model of WM that incorporates four pools of magnetization, based on published in vitro measurements. Next, the experimental reproducibility of each technique was investigated, and the impact of certain basic variations in the data acquisition and analysis procedures was evaluated. In the final stage, both methods were applied longitudinally in vivo to assess the dynamic changes that occur in acute, contrast-enhancing lesions of MS. The theoretical results illustrate the sensitivity and limitations of QMTI and QT2 to specific pathology-inspired modifications of WM, and shed new light on the potential specificity of often-neglected QMTI parameters. The reproducibility of both techniques is acceptable for use in repeated clinical measurements, and QMTI has lower variability overall. The importance of corrections for magnetic field inhomogeneity in QMTI is demonstrated, and a simple optimization of the QMTI data acquisition is introduc
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".