Miniature C-arm simulator using wireless accelerometer based tracking
Bibliographic record
Abstract
C-Arm positioning for interventional spine procedures can often be associated with a steep learning curve. The current training standards involve using real X-rays on cadavers or via apprenticeship-based programs. To help limit excess radiation exposure, several radiation-free training systems have been proposed in the literature but there lacks a hands-on, cost-effective simulator that does not require access to a physical C-Arm. In order to expand the accessibility of radiation-free C-Arm training, we have developed a 10:1 scaled down C-Arm simulator using 3D-printed parts and wireless accelerometers for tracking. We generated Digitally Reconstructed Radiographs (DRRs) in real-time using a 1-dimensional transfer function operating on a ray-traced projection of a patient CT scan. To evaluate the efficacy of the system as a training tool, we conducted a user study in which anesthesiology and orthopedic residents were evaluated on the accuracy of their C-Arm placement for three standard views used in spinal injection procedures. Both the experimental group and control group were given the same evaluation task with the experimental group receiving 5 minutes of training on the system using real-time DRRs and a standardized two page curriculum on proper image acquisition. The experimental group achieved an angular error of 4.76±1.66° which was lower than the control group at 6.88±3.67° and the overall feedback of the system was positive based on a Likert scale questionnaire filled out by each participant. The results indicate that our system has high potential for improving C-Arm placement in interventional spine procedures and we plan to conduct a follow-up study to evaluate the long-term training capabilities of the simulator.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".