Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent <i>T</i><sub>2</sub> relaxometry: A unified view via a four‐pool model
Bibliographic record
Abstract
Nuclear magnetic resonance relaxation and magnetization transfer in cerebral white matter can be described using a four-pool model: two for water protons (in separate myelin and intra/extracellular compartments) and two for protons associated with the lipids and proteins of biologic membranes (of myelin and nonmyelin semisolids). This model was used to gain insight into the observations from multicomponent quantitative T(2) relaxometry and quantitative magnetization transfer imaging, both based on simplified white matter models and experimentally feasible in vivo. Using a set of coupled Bloch equations describing the behavior of the magnetization in a four-pool model of white matter, simulations of the quantitative T(2) relaxometry and quantitative magnetization transfer imaging techniques were performed. Pathology-inspired modifications were made to the four-pool model to gauge their impact on quantitative T(2) relaxometry and quantitative magnetization transfer imaging observations. Our results show that changes in the rate of water movement between microanatomic compartments may impact otherwise stable quantitative T(2) relaxometry observations; that the measure of the quantitative magnetization transfer imaging-based semisolid pool population is robust, despite the presence of two distinct semisolid components; and that quantitative magnetization transfer imaging compartment size estimates are not influenced by changes in the T(2) of the intra/extracellular water pool. The four-pool model, while impractical for in vivo characterization, yields important insight into the interpretation of changes observed with these quantitative MRI methods based on simplified models of white matter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".