C-Arm and Patient Table Integrated Kinematics and Surgical Workspace Analysis
Bibliographic record
Abstract
X-rays are extensively used in minimally invasive surgeries. C-arm devices used for X-ray imaging are usually repositioned multiple times until the desired anatomy is properly viewed. In this work, the C-arm kinematic chain is integrated with patient table to increase degrees of freedom (DOF) and surgical workspace which will enable capturing anatomies from additional poses. A collision detection algorithm was developed to detect collisions between the C-arm and patient table using their 3D models. Using the collision detection algorithm and integrated kinematics, surgical workspace analysis was performed for typical clinical interventional projections and varying DOF setups. Moving the patient table showed more collision-free workspace for multiple clinical interventional projections compared to moving the C-arm device alone. Additionally, moving both the C-arm and patient table were key to attain many clinical interventional poses and overall, significantly increased collision-free workspace. To provide benchmark, a numerical iterative method was used to solve inverse kinematics by optimizing setups with varying DOF for different clinical interventional projections. A collision-free dataset consisting of random joint configurations and their corresponding target poses was created, and the code is made publicly available to enable researchers to further develop inverse kinematics algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".