Sci‐AM1 Sat ‐ 08: Towards MR‐based treatment planning: Characterisation of geometric distortion in 3T MR images
Bibliographic record
Abstract
Because of the excellent soft‐tissue detail provided by MR images, it is the optimum imaging modality for treatment planning target delineation. While the structure of a tumor can be seen in great detail on MR images, the geometric accuracy of the images is limited by the homogeneity of the background field, the linearity of the applied gradients, and the magnetic susceptibility of the imaged tissues. As such, MR images cannot be used alone for novel treatment planning purposes (i.e. MR simulation), or in conjunction with CT because of geometric distortion. Our research seeks to quantify the amount of distortion in 3T MR images due to both background inhomogeneities and gradient nonlinearities on a sequence by sequence basis by using a specialized grid phantom and an in‐house developed software program. The matlab‐based program accurately determines the 3D coordinates of over 9000 control points distributed throughout the phantom's volume. Three dimensional distortion maps can be generated by comparing the control point coordinates determined from an MR scan to the control point coordinates determined from a CT scan. Control point locations can be determined to an accuracy of 0.2 mm and distortions as large as 13 mm have been measured. With appropriate post‐processing correction factors derived from the 3D distortion maps, MR images can be undistorted and either combined or used individually for new treatment planning methods that benefit from the superior soft‐tissue information that magnetic resonance techniques provide.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".