Correcting magnetic resonance ‐space data for in‐plane motion using an optical position tracking system
Bibliographic record
Abstract
PURPOSE: Motion is a major confound of image quality in MRI. A method of retrospectively correcting the effects of rotations and translations on the acquired k-space data is presented. METHODS: In two phantom experiments of well-controlled translation and rotation, two MRI-compatible infrared cameras recorded motion data that were used subsequently to correct the position and phase of recorded k-space samples. Motion data can be acquired with a temporal resolution of 60 Hz and spatial accuracy of 0.1 mm for translations and 0.2 degree for rotations. RESULTS: Significant improvements of image quality are demonstrated. CONCLUSIONS: The key advantages of the technique are that it is easy to implement, does not interfere with or complicate MR data acquisition, and is capable of correcting distortions within a single slice. Therefore, the technique has the potential to improve upon approaches that rely on the registration or realignment of successive imaging slices.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".