Augmented Reality Visualization Using Image Overlay Technology for MR-Guided Interventions
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to prospectively test the hypothesis that image overlay technology facilitates accurate navigation for magnetic resonance (MR)-guided osseous biopsy. MATERIALS AND METHODS: A prototype augmented reality image overlay system was used in conjunction with a clinical 1.5-T MR imaging system. Osseous biopsy of a total of 16 lesions was planned in 4 human cadavers with osseous metastases. A loadable module of 3D Slicer open-source medical image analysis and visualization software was developed and used for display of MR images, lesion identification, planning of virtual biopsy paths, and navigation of drill placement. The osseous drill biopsy was performed by maneuvering the drill along the displayed MR image containing the virtual biopsy path into the target. The drill placement and the final drill position were monitored by intermittent MR imaging. Outcome variables included successful drill placement, number of intermittent MR imaging control steps, target error, number of performed passes and tissue sampling, time requirements, and pathological analysis of the obtained osseous core specimens including adequacy of specimens, presence of tumor cells, and degree of necrosis. RESULTS: A total of 16 osseous lesions were sampled with percutaneous osseous drill biopsy. Eight lesions were located in the osseous pelvis (8/16, 50%) and 8 (8/16, 50%) lesions were located in the thoracic and lumbar spine. Lesion size was 2.2 cm (1.1-3.5 cm). Four (2-8) MR imaging control steps were required. MR imaging demonstrated successful drill placement inside 16 of the 16 target lesions (100%). One needle pass was sufficient for accurate targeting of all lesions. One tissue sample was obtained in 8 of the 16 lesions (50%); 2, in 6 of the 16 lesions (38%); and 3, in 2 of the 16 lesions (12%). The target error was 4.3 mm (0.8-6.8 mm). Length of time required for biopsy of a single lesion was 38 minutes (20-55 minutes). Specimens of 15 of the 16 lesions (94%) were sufficient for pathological evaluation. Of those 15 diagnostic specimens, 14 (93%) contained neoplastic cells, whereas 1 (7%) specimen demonstrated bone marrow without evidence of neoplastic cells. Of those 14 diagnostic specimens, 11 (79%) were diagnostic for carcinoma or adenocarcinoma, which was concordant with the primary neoplasm, whereas, in 3 of the 14 diagnostic specimens (21%), the neoplastic cells were indeterminate. CONCLUSIONS: Image overlay technology provided accurate navigation for the MR-guided biopsy of osseous lesions of the spine and the pelvis in human cadavers at 1.5 T. The high technical and diagnostic yield supports further evaluation with clinical trials.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".