Robust correction of spike noise: Application to diffusion tensor imaging
Bibliographic record
Abstract
Echo-planar imaging (EPI) -based diffusion tensor imaging (DTI) is particularly prone to spike noise. However, existing spike noise correction methods are impractical for corrupted DTI data because the methods correct the complex MRI signal, which is not usually stored on clinical MRI systems. The present work describes a novel Outlier Detection De-spiking technique (ODD) that consists of three steps: detection, localization, and correction. Using automated outlier detection schemes, ODD exploits the data redundancy available in DTI data sets that are acquired with a minimum of six different diffusion-weighted images (DWIs) with similar signal and noise properties. A mathematical formulation, describing the effects of spike noise on magnitude images, yields appropriate measures for an outlier detection scheme used for spike detection while a normalization-dependent outlier detection scheme is used for spike localization. ODD performs accurately on diverse DTI data sets corrupted by spike noise and can be used for automated control of DTI data quality. ODD can also be extended to other MRI applications with data redundancy, such as dynamic imaging and functional MRI.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".