Improved T/sub 2/ and diffusion maps from wavelet de-noised magnetic resonance imaging data
Bibliographic record
Abstract
By appropriately configuring acquisition parameters, magnetic resonance (MR) images can be influenced by physical factors that include spin-spin (T/sub 2/) relaxation rates and diffusion coefficients of the imaged tissues. Usually MR images are the result of a dominating influence of one of these factors to produce T/sub 2/-weighted, diffusion-weighted, etc. images. But, increasingly, radiologists and researchers are interested in direct measurement of the physical parameters. This involves the use of two or more images in a calculation that will reveal T/sub 2/ maps, diffusion maps, etc. The error introduced by the calculation can be larger than the measurement errors in the original base images so these should be relatively noise-free. Here it is shown how a wavelet based de-noising algorithm can be used to preprocess the base images prior to map calculation to produce improved diffusion and T/sub 2/ maps from MR image data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".