A two‐step scheme for distortion rectification of magnetic resonance images
Bibliographic record
Abstract
The aim of this work is to demonstrate a complete, robust, and time-efficient method for distortion correction of magnetic resonance (MR) images. It is well known that MR images suffer from both machine-related spatial distortions [gradient nonlinearity and main field (B0) inhomogeneity] and patient-related spatial distortions (susceptibility and chemical shift artifacts), and growing interest in the area of MR-based radiotherapy treatment planning has put new requirements on the geometric accuracy of such images. The authors present a two-step method that combines a phantom-based reverse gradient technique for measurement of gradient nonlinearities and a patient-based phase difference mapping technique for measurement of B0 inhomogeneities, susceptibility, and chemical shift distortions. The phase difference mapping technique adds only minutes to the total patient scan time and can be used to correct a variety of images of the same patient and anatomy. The technique was tested on several different phantoms, each designed to isolate one type of distortion. The mean distortion was reduced to 0.2 +/- 0.1 mm in both gradient echo and spin echo images of a grid phantom. For the more difficult case of a highly distorted echo planar image, residual distortion was reduced to subvoxel dimensions. As a final step, the technique was implemented on patient images. The current technique is effective, time efficient, and robust and provides promise for preparing distortion-rectified MR images for use in MR-based treatment planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".