SU‐F‐J‐154: Harmonic Analysis for Arbitrary 3D MR Image Distortion Fields
Bibliographic record
Abstract
Purpose: To study the practical feasibility of harmonic analysis for the full quantification of 3D MR image distortion fields associated with arbitrarily‐shaped regions of interest. Methods: The 3D scanner‐related distortion field (F) is innate to the MR image data. Since it is implicitly related to the magnetic fields required to perform MR imaging, F is in a steady‐state and can be described by a boundary value problem in which the Laplace's equation is solved with Dirichlet boundary conditions. Specifically, to fully derive a unique solution for F inside an arbitrarily‐shaped domain (D) the minimum input data required is given by the boundary of D. In practice, the data that needs to be measured by means of image acquisition and post‐processing is on the 3D surface of D (e.g. ellipsoid, cylinder, cuboid). Data from a large FOV phantom with a dense grid structure was used as a reference to explore multiple scenarios. The harmonic analysis solver was based on a finite element method and the domains were defined as binary STL structures for robust meshing. Simple and complex (non‐)quadratic shapes were analyzed to test the applicability of the harmonic approach. The harmonic solution and reference data were compared inside D using histograms and typical metrics such as max/mean/stdev using a residual error threshold set by the accuracy of the reference data (i.e. 1 mm). Results: The harmonic analysis showed good performance for all cases investigated. The percentage of control points inside D exhibiting residual values above 1 mm was lower than 1%. Conclusion: Harmonic analysis can be used to characterize geometric distortions inside arbitrary volumes given availability of the surface data with a certain 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".