WE‐G‐WAB‐03: 4D Composite MR Image Distortion Field: Quantification and Applications for MRI‐Guided Radiotherapy
Bibliographic record
Abstract
Purpose: To study the interplay effects of MR image distortion fields for moving/deformable anatomy and their implications for MR‐guided radiotherapy. Methods: The composite distortion field is given by the combination of two independent components a) the system‐related distortions due to B0 inhomogeneities and imaging gradients nonlinearities and b) the tissue magnetic susceptibility‐induced effects. Field a) is static while b) is dynamic, modifying as a function of tissue interface characteristics (shape, location, type of neighboring tissues). The field experienced by a moving or deformable (over time) target/organ can be very complex as entity changes location and shape. Field a) was measured with a linearity object in conjunction with a harmonic analysis. Field b) was computed numerically using a finite difference method. Both fields were quantified for moving/deformable anatomy using multiple image data bins derived from 4D CT and 4D MRI for several lung and liver cases. Results: The 4D composite field tools and methodology was validated in phantoms. The independent and combined distortion fields were quantified for several lung and liver patients. For a lung case with a target travelling 2.4 cm in the superior‐inferior direction, the susceptibility distortions for the inhale and exhale phases were 3.84 and 2.5 ppm, respectively. The system distortion difference for the two phases was about 1 mm. The maximum 4D composite distortion between the inhale and exhale was approximately 1.5 mm. For liver, the target experienced negligible distortions (embedded in soft‐tissue) and the diaphragm showed a local distortion larger than 1 mm. Conclusion: The combined effect due to various types of MR image distortions were quantified for organ motion/deformation. Geometric distortion introduced by the 4D composite field may be significant for certain scenarios. However, this can be mitigated by a careful selection of imaging and by implementing robust methods for image 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 | 0.002 |
| 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) | 0.002 | 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".