Clinical Image Coregistration Variability on a Dedicated Radiosurgery Unit
Bibliographic record
Abstract
BACKGROUND: On a new dedicated radiosurgery unit enabling frameless treatments, a cone-beam computed tomography (CBCT) can be used for stereotactic definition. Since magnetic resonance imaging (MRI) is used to delineate target, reproducible MRI-to-CBCT coregistration is vital for accurate target localization. OBJECTIVE: To evaluate reproducibility of image coregistration in patient images. METHODS: Three types of coregistration (source-to-target) were analyzed: (1) MRI-to-CT; (2) MRI-to-CBCT; and (3) CT-to-CBCT. For each patient (n = 15), each coregistration type was independently performed 5 to 30 times (total: 465 coregistrations). Each coregistration yielded a transformation matrix, which was subsequently applied to transform every point in the source image to stereotactic coordinates. Two metrics were measured: (1) target registration error (TRE): mean distance between the registered position of each target point and the average registration position of that point; (2) compound registration error (CRE): mean spatial difference between stereotactic coordinates using (A) MRI-to-CT-to-CBCT and (B) MRI-to-CBCT. RESULTS: The median (range) of TRE was 0.11 mm (0.06-0.22 mm), 0.17 mm (0.10-0.36 mm), and 0.12 mm (0.08-0.21 mm) for MRI-to-CT, MRI-to-CBCT, and CT-to-CBCT, respectively. The TRE for MRI-to-CBCT was statistically higher than the other 2 methods (P < .01). The median (range) of CRE was 0.44 mm (0.22-0.59 mm). The maximum point CRE between patients ranged from 0.37-1.15 mm when considering all MRI points, but reduced to 0.31-0.90 mm within the central 16 cm. The CRE varied across the image volume, and typically was minimized near the center. CONCLUSION: The variation in image coregistration is within 0.2 mm, indicating a high degree of reproducibility. The CRE varies throughout the head but is submillimeter in the central 16 cm region.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".