Trajectory Planning for Multiple Degrees of Freedom C-Arm Systems
Bibliographic record
Abstract
This paper presents a comprehensive analysis of the kinematic and motion planning capabilities of advanced C-arm systems with varying degrees of freedom (DoF) to enhance precision, adaptability, and performance in clinical imaging applications. Five configurations of C-arm systems were implemented ranging from 5-DoF to 9 DoF, with additional DoF incorporated through an operating table which expands workspace accessibility. For clinical validation, six clinical projections were analyzed through workspace analysis, resulting in the generation of collision-free pose datasets with varying sizes specific to each projection. To optimize trajectory planning, the Joint Space Motion Model in MATLAB Simulink was employed to evaluate four trajectory profiles: Trapezoidal Velocity Profile, Polynomial, Minimum Jerk Polynomial, and Minimum Snap Polynomial. Computed Torque Control was integrated into the Joint Space Motion Model to ensure dynamic stability and precise motion execution. Six trajectory trials were conducted involving sequential transitions across clinical projections, with simulations performed under normal and overweight patient conditions to evaluate the impact of loading conditions on positional accuracy and trajectory performance. Results demonstrate clinically acceptable tolerances within ±1mm and ±1°. Additionally, lookup tables (LUT) were developed to guide clinicians in selecting appropriate trajectories and tuning joint parameters for desired positions, providing practical insights into minimizing positional errors. To bridge the gap between theoretical modeling and clinical practice, lookup tables were developed to guide clinicians in selecting trajectory types and tuning joint parameters. The study establishes the first modular trajectory planning framework for multi-DoF C-arm systems, highlighting the superiority of higher DoF configurations and polynomial-based trajectories in achieving smooth, precise, and collision-free motion for surgical imaging applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".