Diffusion-Weighted Magnetic Resonance Imaging Attenuation Factors and Their Selection for Cancer Diagnosis and Monitoring
Bibliographic record
Abstract
Diffusion-weighted imaging (DWI) is based on the detection of water molecule movement in interstitial and intracellular space, and that motion may be restricted in ischemia and in tumors. An early diagnosis and characterization of several cancer related diseases is possible with DWI. Diffusion-weighted images are therefore important for patient management. Knowledge of the technical requirements for DWI, including a suitable selection of b-values for differentiating between perfusion and true diffusion, as well as an understanding of the advantages and limitations of different b-values selections, is necessary to obtain reliable diagnostic results. The aim of this article is to review the fundamentals of the DWI technique, the common protocols used, b-value selection, and DWI's main contribution to neoplasm detection and staging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".