Resolution and registration in dual-plane co-RASOR MR
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) has superior soft tissue contrast and lower interobserver variability compared to computed tomography and advances in equipment and pseudo-CT estimation have allowed for MR-only radiation therapy planning. Dedicated MR sequences have been used to localize paramagnetic structures with positive contrast, and most implanted seeds are gold fiducial markers (GFMs). We used a fast, dual-plane co-RASOR sequence to localize implanted GFMs with positive contrast in phantom and tissue to assess their resolution and registration accuracy of registration to CT. Off-resonant reconstructions of co-RASOR images were able to resolve GFMs down to 5 mm apart at 12 cm FOV. No systematic biases were observed by comparing registration of co-RASOR and bSSFP to CT images in an MR-compatible Lego phantom with a set of highly visible known points. The standard deviations of the MR to CT distance errors were <0.5 mm in all directions. We separated the component due to registration by comparing the two MR sequences, which had a maximum standard deviation of 0.36 mm in the SI-direction. Registration using the positive contrast points in a porcine sample phantom showed increased errors, but co-RASOR still performs acceptably with a target registration error of <0.75 mm. The dual-plane co-RASOR sequence could then be used for both registration and image tracking when performing MR-only radiation therapy planning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".