TU‐G‐134‐02: A Harmonic Field Approach to Quantifying MRI Spatial Accuracy for MRIgRT
Bibliographic record
Abstract
Purpose: An outstanding problem in the clinical implementation of MRIgRT is the development of simple and efficient methods for the measurement, characterization, and monitoring of geometrical accuracy in MRI. This study presents the theory, implementation, and validation of a novel harmonic field approach to the quantification of spatial accuracy in MRI. Methods: A harmonic field description of the 3D distortion vector field in MRI is derived as the solution of a second‐order boundary value problem comprised of the Laplace equation and a limited measurement of the distortion at a sparse distribution of points. Harmonic representations of the distortion field were calculated for spherical, cylindrical, and irregular regions of interest (ROIs). These representations were then used to derive volumetric mappings of the distortion field that were compared against reference data obtained on a 3 T full‐body scanner. The effects of sampling density were explored and a statistical uncertainty analysis was performed. Results: The volumetric mappings of the distortion field derived from the harmonic analysis were found to be in excellent agreement with the reference data. For all ROI geometries and distortion vector components, the 1 mm3 voxel size of the images used to acquire the reference data was greater than at least two standard deviations in all discrepancies. The statistical uncertainty analysis demonstrated the robustness of the harmonic approach, as the uncertainties in the volumetric mappings were calculated to be less than or equal to the input reference data. Conclusion: A novel harmonic approach has been proposed for quantifying MRI spatial accuracy in large imaging volumes for MRIgRT using only limited measurement data. This technique abates the requirement to directly measure the distortion at a dense 3D array of points and thus permits the design of simple, inexpensive phantoms that may feature additional modules for supplementary imaging and dosimetry QA objectives.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".