Design and implementation of a 3D-MR/CT geometric image distortion phantom/analysis system for stereotactic radiosurgery
Bibliographic record
Abstract
Abstract The design, construction and application of a multimodality, 3D magnetic resonance/computed tomography (MR/CT) image distortion phantom and analysis system for stereotactic radiosurgery (SRS) is presented. The phantom is characterized by (1) a 1 × 1 × 1 (cm) 3 MRI/CT-visible 3D-Cartesian grid; (2) 2002 grid vertices that are 3D-intersections of MR-/CT-visible ‘lines’ in all three orthogonal planes; (3) a 3D-grid that is MR-signal positive/CT-signal negative; (4) a vertex distribution sufficiently ‘dense’ to characterize geometrical parameters properly, and (5) a grid/vertex resolution consistent with SRS localization accuracy. When positioned correctly, successive 3D-vertex planes along any orthogonal axis of the phantom appear as 1 × 1 (cm) 2 -2D grids, whereas between vertex planes, images are defined by 1 × 1 (cm) 2 -2D arrays of signal points. Image distortion is evaluated using a centroid algorithm that automatically identifies the center of each 3D-intersection and then calculates the deviations d x , d y , d z and d r for each vertex point; the results are presented as a color-coded 2D or 3D distribution of deviations. The phantom components and 3D-grid are machined to sub-millimeter accuracy, making the device uniquely suited to SRS applications; as such, we present it here in a form adapted for use with a Leksell stereotactic frame. Imaging reproducibility was assessed via repeated phantom imaging across ten back-to-back scans; 80%–90% of the differences in vertex deviations d x , d y , d z and d r between successive 3 T MRI scans were found to be ⩽0.05 mm for both axial and coronal acquisitions, and over >95% of the differences were observed to be ⩽0.05 mm for repeated CT scans, clearly demonstrating excellent reproducibility. Applications of the 3D-phantom/analysis system are presented, using a 32-month time-course assessment of image distortion/gradient stability and statistical control chart for 1.5 T and 3 T GE Twin Speed MRI systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".