Axial Motion Compensation Of Optical Coherence Tomography With A Collaborative Medical Robot
Bibliographic record
Abstract
This thesis presents an imaging tool consisting of an Optical Coherence Tomography (OCT) imaging system mounted on a collaborative robotic arm to enable axial motion compensation. Optical Coherence Tomography is a subsurface, high-resolution imaging modality used in neuroimaging to differentiate between pathological and non-pathological tissue. The motivation behind this project is to bring Optical Coherence Tomography to the operating room for neuroimaging to help with cancerous tissue differentiation and maximize the extent of tumor resection. However, neurosurgeons have expressed concern with respect to intracranial pressure (ICP) pulsation displacing the brain far off the optic axis of the imaging system so as to not be visible. The collaborative robotic arm compensates for sample motion along the optic axis using a Proportional controller to track the position of the peak intensity of the sample’s intensity profile, which generally corresponds to the sample surface. Collaborative robots have changed the robot industry paradigm becoming increasingly functional and safer than the previous generations of robotic arms. We present an OCT robot end-effector to test the feasibility of performing OCT imaging with the collaborative robot.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".