Sci‐Fri AM: YIS‐01: Comprehensive MR distortion correction: Phantom validation and in‐vivo application
Bibliographic record
Abstract
MR images provide excellent diagnostic information; however, their treatment planning utility is limited due to geometric uncertainties from both system and patient related sources. Despite this concern, interest in developing MR‐based treatment planning protocols is on the rise because of the ease with which clinically relevant structures can be identified in MR. Here we present our systematic approach to quantifying both machine (gradient non‐linearity and B0 inhomogeneity) and patient (susceptibility and chemical shift) distortions. Gradient non‐linearities were previously measured using a 3D grid phantom while the remaining types of distortion were measured using a double gradient echo scan to obtain a B0 distortion map specific to each object/patient. Distortion measurement and correction were validated on phantoms and then implemented on a volunteer. B0 inhomogeneity and susceptibility distortions were simulated by offsetting the x2‐y2 shims; maximum absolute distortion was reduced from 5.4 mm to 1.0 mm and mean (± standard deviation) was reduced from 1.7 ± 1.4 mm to 0.4 ± 0.2 mm. Chemical shift distortion was qualitatively evaluated using a phantom containing fat and water inserts; displacement of the fat signal was much improved following distortion correction. Intensity correction was validated using a uniformity phantom and undistorted image profiles were compared to distorted image profiles and to profiles corrected for geometric and geometric/intensity distortion; the need for intensity correction was clearly demonstrated. Once all types of distortion correction were validated on phantoms, the technique was implemented on a volunteer brain image. Both GE and multi‐shot EPI images were corrected.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".