Stereotactic radiosurgery planning of vestibular schwannomas: Is<scp>MRI</scp>at 3 Tesla geometrically accurate?
Bibliographic record
Abstract
Purpose MRIis a mandatory requirement to accurately plan Stereotactic Radiosurgery (SRS) for Vestibular Schwannomas. However,MRImay be distorted due not only to inhomogeneity of the static magnetic field and gradients but also due to susceptibility‐induced effects, which are more prominent at higher magnetic fields. We assess geometrical distortions around air spaces and considerMRIprotocol requirements forSRSplanning at 3 T. Methods Hardware‐related distortion and the effect of incorrect shimming were investigated with structured test objects. The magnetic field was mapped over the head on five volunteers to assess susceptibility‐related distortion in the naso‐oro‐pharyngeal cavities (NOPC) and around the internal ear canal (IAC). Results Hardware‐related geometric displacements were found to be less than 0.45 mm within the head volume, after distortion correction. Shimming errors can lead to displacements of up to 4 mm, but errors of this magnitude are unlikely to arise in practice. Susceptibility‐related field inhomogeneity was under 3.4 ppm, 2.8 ppm, and 2.7 ppm for the head,NOPCregion andIACregion, respectively. For theSRSplanning protocol (890 Hz/pixel, approximately 1 mm3isotropic), susceptibility‐related displacements were less than 0.5 mm (head), and 0.4 mm (IACandNOPC). Large displacements are possible inMRIexaminations undertaken with lower receiver bandwidth values, commonly used in clinicalMRI. Higher receiver bandwidth makes the protocol less vulnerable to sub‐optimal shimming. The shimming volume and theCT‐MRco‐registration must be considered jointly. Conclusion Geometric displacements can be kept under 1 mm in the vicinity of air spaces within the head at 3 T with appropriate setting of the receiver bandwidth, correct shimming and employing distortion correction.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".