Correction for geometric distortion and N/2 ghosting in EPI by phase labeling for additional coordinate encoding (PLACE)
Why this work is in the frame
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Bibliographic record
Abstract
Echo-planar imaging (EPI) is vulnerable to geometric distortion and N/2 ghosting. These artifacts can be analyzed with an intuitive k-t space tool, and here we propose a simple method for their correction. In a slightly modified additional EPI acquisition, we sample the k-t space with a shift in k(y) by adding a small area to the phase-encoding (PE) gradient. Physically, the added gradient area creates a relative phase ramp across the object and directly encodes the undistorted original y-coordinate of each voxel into a phase difference between two distorted complex images, in a method called "phase labeling for additional coordinate encoding" (PLACE). The phase information is then used to map the mismapped signals back to their original locations for geometric and intensity correction. Smoothing of expanded complex data matrix effectively reduces noise in the differential phase map and allows subpixel warping. The two acquired images can also be averaged to effectively suppress the N/2 ghost. Efficient correction for both artifacts can be achieved with three acquisitions. These acquisitions can also serve as reference scans to correct for geometric distortion and/or N/2 ghost artifacts on all images in a time series. The technique was successfully demonstrated in phantom and animal studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it