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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".