Poster — Thur Eve — 75: Towards MR only simulation: MR based digitally reconstructed radiograph of head and neck
Bibliographic record
Abstract
PURPOSE: To develop a practical method to obtain bony structures from Magnetic Resonance (MR) images and to create MR- based digitally reconstructed radiographs (DRR) for MR only simulation. METHODS AND MATERIALS: Using T1-weighted MR images, air regions including the sinuses and the airway in the head and neck were manually contoured. The bone and soft tissue masks were automatically generated based on the statistical data calculated from the air contour and MR intensities. "CT like" MR images were generated by mapping the MR intensities of the voxels within these masks into the CT number ranges of these tissues. The MR-based DRRs created from "CT like" MR images were quantitatively evaluated using the co-registered MR and CT images of 10 stereotactic radiosurgery CNS patients. Ten anatomical control points, set on the contours of the skull segmented using a threshold of 300 HU were used to determine the differences in distance between MR-based DRRs and CT-based DRRs, and to evaluate the geometrical accuracy of MR-based DRRs. RESULTS: The bony structures were visible in the MR-based DRRs. The mean geometric difference and standard deviation between the ten anatomical control points on MR- based and CT-based DRRs were -0.03±1.11 mm (including uncertainty of image fusion). The maximum distance difference was 1.67mm. CONCLUSIONS: The study provides a practical method to generate MR- based DRRs from MR-only simulations of the head and neck regions. The image quality and anatomical accuracy of MR-based DRRs is comparable to that of CT-based DRRs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".