Comparison of Multiecho Postprocessing Schemes for SWI with Use of Linear and Nonlinear Mask Functions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: SWI is an MR technique conventionally implemented with single-echo gradient-echo data. The purpose of this study was to compare single-echo SWI processing and 2 multiecho SWI processing schemes: postaverage, where an SWI image is created for each echo and then averaged to create a single volume; and frequency-based, where a SWI image is generated from an average frequency image. Linear and nonlinear mask functions were investigated for all 3 processing schemes. MATERIALS AND METHODS: Comprehensive optimizations were performed. Single and multigradient-echo data were acquired at 3T in 10 volunteers. Contrast-to-noise ratio was measured in various structures. Visibilities of the same structures were ranked in different SWI images by trained raters. RESULTS: When image evaluation was based on measurements of contrast-to-noise ratio, the nonlinear mask and frequency-based scheme were superior. However, when image evaluation was based on ranks of qualitative visibility, the linear mask and postaverage scheme were superior. Although the nonlinear mask and frequency-based scheme allow increased contrast of paramagnetic perturbers such as the globus pallidus, periventricular veins, red nucleus, and subthalamic nucleus, they do not necessarily increase the information content of the image; rather, they result in a harsh contrast that is visually unpleasing to radiologists and wherein more subtle structure is relatively less apparent. CONCLUSIONS: Linearly masked postaverage SWI is the recommended implementation of multiecho SWI for radiologic use; however, nonlinearly masked frequency-based SWI may have use in computer-based segmentation or registration.
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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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".