Characterization and inter‐scanner reproducibility of geometric distortion on a small footprint, high‐performance, head‐specific 0.5 T scanner
Bibliographic record
Abstract
Abstract Background Magnetic resonance imaging (MRI) offers superior soft tissue contrast and essential imaging capabilities for modern medicine. MRI is increasingly being used in applications that require a high degree of spatial fidelity; however, distortions are a well‐known limitation of the modality. The mid‐field (0.3 T ≤ B 0 < 1 T) has advantages in this respect due to being less susceptible to patient‐induced distortions. Purpose The purpose of this work was to characterize the geometric fidelity of a short‐bore, head‐specific, 0.5T MRI system. Methods Assessment of spatial fidelity was performed using a 3D gradient recalled echo (GRE) acquisition on a commercial distortion phantom using the validated distortion analysis software provided. B 0 ‐induced distortions were measured using a 3D field map. Inter‐scanner reproducibility was assessed across four distinct systems of identical make and model, while intra‐scanner repeatability was assessed at one site over six repeat measurements. Results Inter‐scanner reproducibility measured an average 95th percentile distortion over 100 and 180 mm DSV of 0.15 ± 0.03 and 0.33 ± 0.05 mm. Average 95th percentile distortions due to B 0 field inhomogeneities over 100 and 180 mm DSV were 0.02 ± 0.01 and 0.07 ± 0.02 mm. Intra‐scanner repeatability measured the uncertainty in distortion values to be 0.020 ± 0.005 mm. Conclusion The total residual distortions measured in this phantom study were less than half the recommended value required for radiosurgery and significantly better than data published from other MR systems. This demonstrates that in addition to the compact footprint of the Synaptive 0.5T scanner, it exceeds current standards for geometric accuracy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".