SU‐FF‐I‐123: Clinical Value of Diffusion‐Weighted MRI in White Matter in Vivo
Bibliographic record
Abstract
The excellent soft tissue contrast of diffusion‐weighted magnetic resonance imaging, DW‐MRI has made it an invaluable technique especially in oncological assessment bringing hope in distinguishing between brain abscesses and necrotic and cystic neoplasms. However, a clear characterization of brain tumors and the associated pathologic structures is still a challenge. In this study, a tissue model is proposed to interpret the water diffusion behaviour in white matter based on the measured apparent diffusivities and their volume fractions. Measurements were performed on a 1.5T clinical scanner (Siemens). The protocol included: DTI measurements (b=0 and 500s/mm2), DW images (96 b‐values ranging from 0 to 10,000s/mm2 with diffusion gradient rotated in the x‐z plane, NEX=6, TE=200ms) and 96‐echoes, non‐linearly sampled using T2‐sequence. All decay curves measured in the splenium agreed well with the assumed bi‐exponential function: . The fast water pool, ADCfast had a long apparent T2 relaxation rate while the slow water pool, ADCslow had a shorter T2 constant. The volume fraction fslow correlate with the volume fraction directly estimated from the T2 spectrum. Markedly anisotropy was observed in ADCfast and fslow and more subtle in ADCslow. Evaluation of water diffusion in normal appearing corpus callosum is clearly assisting the peculiar properties of water in biological system, invoking long‐range hydration structures modulated by the concentration of lipid‐protein complexes in the membranes, cellular size and tissue architecture. ADCslow originate from the water in the hydration layers, as ADCfast the rest of the tissue bulk water. The membrane and associated hydration layers form a barrier to water diffusion perpendicular to them since diffusion within the hydration layers is anisotropic. This model is very sensitive to cellularity and further to a brain tumor grade; cell swelling and increased density of membranes enlarge fslow resulting in a decreased ADCs linked to cell proliferation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".