1.5 T augmented reality navigated interventional MRI: paravertebral sympathetic plexus injections
Bibliographic record
Abstract
PURPOSE: The high contrast resolution and absent ionizing radiation of interventional magnetic resonance imaging (MRI) can be advantageous for paravertebral sympathetic nerve plexus injections. We assessed the feasibility and technical performance of MRI-guided paravertebral sympathetic injections utilizing augmented reality navigation and 1.5 T MRI scanner. METHODS: A total of 23 bilateral injections of the thoracic (8/23, 35%), lumbar (8/23, 35%), and hypogastric (7/23, 30%) paravertebral sympathetic plexus were prospectively planned in twelve human cadavers using a 1.5 Tesla (T) MRI scanner and augmented reality navigation system. MRI-conditional needles were used. Gadolinium-DTPA-enhanced saline was injected. Outcome variables included the number of control magnetic resonance images, target error of the needle tip, punctures of critical nontarget structures, distribution of the injected fluid, and procedure length. RESULTS: Augmented-reality navigated MRI guidance at 1.5 T provided detailed anatomical visualization for successful targeting of the paravertebral space, needle placement, and perineural paravertebral injections in 46 of 46 targets (100%). A mean of 2 images (range, 1-5 images) were required to control needle placement. Changes of the needle trajectory occurred in 9 of 46 targets (20%) and changes of needle advancement occurred in 6 of 46 targets (13%), which were statistically not related to spinal regions (P = 0.728 and P = 0.86, respectively) and cadaver sizes (P = 0.893 and P = 0.859, respectively). The mean error of the needle tip was 3.9±1.7 mm. There were no punctures of critical nontarget structures. The mean procedure length was 33±12 min. CONCLUSION: 1.5 T augmented reality-navigated interventional MRI can provide accurate imaging guidance for perineural injections of the thoracic, lumbar, and hypogastric sympathetic plexus.
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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) | 0.000 | 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 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".