O-008 High-frequency optical coherence tomography for imaging neurovascular implants in tortuosity
Bibliographic record
Abstract
Introduction Intravascular optical coherence tomography (OCT) has an established role in the diagnosis and image guided treatment of coronary artery disease. However, due to the profile and design of the catheter, these devices are not suitable for routine application in neurointerventional surgery. We aim to demonstrate that new generation high frequency OCT (HF-OCT) can image stents and flow diverters used to treat intracranial aneurysms with acceptable workflow deployed in neurointerventional surgery. Materials and methods An HF-OCT prototype was built (Gentuity, Sudbury MA) that is compatible with standard microcatheters (0.017’). An in vitro flow loop consisting of a patient-specific vascular replica and using porcine blood as a working fluid was deployed to test contrast infusion protocols necessary to remove blood from the target vascular section. Subsequently, eight pigs were implanted bilaterally with flow diverters and stents. The presence of acute platelet aggregation on the surface of the devices and malapposition of the device to the vessel wall was assessed by 3 raters reviewing digital subtraction angiography (DSA), high resolution cone beam CT (CBCT), and HF-OCT. Finally, using the tortuous porcine brachial artery, HF-OCT was performed, and image quality assessed. Results In the in vitro experiment, the optimized contrast infusion protocol was delivery of contrast through the intermediate catheter (0.058’ Navien, Medtronic Neurovascular, Irvine CA) delivering the HF-OCT device at a rate of 5 ml/s for 20–25 ml of contrast. This protocol resulted in complete clearing of highly attenuating erythrocytes for an optically clear field. In the pig study, the agreement by the reviewers (Fleiss kappa) regarding clot formation at 3 distinct locations along the flow diverter was 0.49, 0.67 and 0.90 for DSA, CBCT and HF-OCT, respectively. The agreement for diagnosing malapposition at these locations was 0.18, 0.67 and 0.87 for DSA, CBCT, and HF-OCT, respectively. In the porcine tortuosity model, in all cases, navigation and imaging was performed with uniform illumination and free from artifact. Conclusion An HF-OCT device has been built that is compatible with neurointerventional surgery workflow and cerebrovascular anatomy. Preclinical data demonstrate consistent improvement to diagnose acute platelet aggregation and malapposition of flow diverters as compared to existing imaging modalities (figure 1). Disclosures A. Puri: 1; C; NIH. M. Marosfoi: None. G. Ughi: 4; C; Gentuity. 5; C; Gentuity. R. King: None. E. Langan: None. J. Chueh: None. M. Gounis: 1; C; NIH.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".