WE‐C‐116‐06: Reducing the Background Field Variations Using the Geometry Information
Bibliographic record
Abstract
Purpose: To demonstrate a new approach for reducing background phase variations in the susceptibility weighted image (SWI)[1]. Methods: In order to perform this experiment, we acquired high resolution sagittal 3D SWI images of the left leg. Background phase variations were removed to significantly reduce the phase due to the geometry and improve the data quality. This was done using forward modeling approach to simulate phase generated only due to the geometry of the leg [2–5]. Results: The collected data indicated that the phase effects due to the background are very large, so we used forward modeling approach to remove the unwanted phase variations in the image background. This approach uses a kernel described by the Green's function in k‐space to generate point dipole effects across the tissues using its magnetic susceptibility properties. The data resulting from this technique gave us improved phase images and also helped in collecting water‐fat out of phase information using complex division of flow compensated (FC) 5.2ms and 6.5ms data sets. To further see the potential of this technique, we complex divided the FC 7.8ms and not FC 7.8ms data sets resulting in data with direct flow information. Conclusion: We have demonstrated that using a homodyne high‐pass filter alone does not remove the phase due to the geometry. Using the proposed novel technique we were able to significantly reduce the phase due to the geometry and improve the quality of the data for further analysis of water and fat separation as well as visualizing veins. The remaining weakness of this method is that the veins appear as fat in the muscle part of the image but the next step in this processing is to due susceptibility mapping which will then remove the veins and leave a pristine fat map. We are currently evaluating this approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".