Multi-scale application of the N3 method for intensity correction of MR images
Bibliographic record
Abstract
Spatial inhomogeneity due to the radio-frequency coil in MR imaging can confound segmentation results. In 1994, Sled introduced the N3 technique, using histogram deconvolution, for reducing inhomogeneity. We found some scans whose steep inhomogeneity gradient was not fully eliminated by N3. We created a multi-scale application of N3 that further reduces this gradient, and validated it on MNI BrainWeb and actual MRI data. The algorithm was applied to proton density simulated BrainWeb scans (with known inhomogeneity) and 100 standard MRI scans. Intra-slice and inter-slice inhomogeneity measures were created to compare the technique with standard N3. The slope of the estimated bias versus the known bias of BrainWeb data was 1.0 (r=0.9844) for N3 and 1.04 (r=0.9828) for multi-scale N3. The bias field estimated by multi-scale N3 was within 1% root-mean-square of that of standard N3. Over 100 MS patient scans, the average intra-slice measure (0 meaning bias-free) was 0.0694 (uncorrected), 0.0530 (N3) and 0.0402 (multi-scale). The average inter-slice measure (1 meaning bias-free) was 0.9121 (uncorrected), 0.9367 (N3) and 0.9508 (multi-scale). The multi-scale N3 algorithm showed a greater inhomogeneity reduction than N3 in the small percentage of scans bearing a strong gradient, and results similar to N3 in the remaining scans.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".