Development of an Intravascular Doppler Optical Coherence Tomography Imaging Technique for Neurovascular Applications
Bibliographic record
Abstract
The rupture of an intracranial aneurysm can cause spontaneous subarachnoid hemorrhage and result in sudden death. A large portion of intracranial aneurysms occurs near the center of the head, at the skull base, which poses significant technical challenge to neurosurgeons due to limited accessibility. The utilization of angiography is prominent during the treatment of intracranial aneurysms. However, malapposition of stent or incomplete packing of the intracranial aneurysm can be difficult to assess with angiography, and could lead to severe postoperative complications. As a result, angiography may not be sufficient in determining the risk of rupture as the compensatory mechanisms are known to occur at the microstructural level due to the local hemodynamics in the arterial lumen, as well as in evaluating the intraoperative treatment. In this work, we describe a method for assessing intracranial aneurysm through the evaluation of blood flow within the lumen and morphological structures of the arterial wall with optical coherence tomography (OCT). Sterile intravascular fiber-optic catheters can be introduced in the artery to detect blood flow. Prior to this work, limited investigations of catheter based Doppler OCT (DOCT) were reported. A novel signal processing technique was developed to further reduce the effect of Doppler noise within a catheter based DOCT system. This technique consisted of splitting the interferogram of an OCT signal prior to estimating the Doppler shift. This split spectrum DOCT (ssDOCT) method was evaluated through flow models and porcine models, as well as through the correlation between ssDOCT algorithm and computational fluid dynamic (CFD) models. It was observed that ssDOCT provided improved Doppler artefact suppression over the conventional DOCT technique. ssDOCT also provided the ability to estimate lower velocities within the DOCT image to measure the hemodynamic patterns around stent struts in both the internal carotid and patient specific flow phantoms. An OCT imaging study was also conducted consisting of surgically resected human intracranial aneurysms. Further enhancement of the detection of these key morphological structures was demonstrated by an optical-attenuation imaging variant of OCT. The presented techniques could provide further insights to the cause of intracranial aneurysm rupture and vascular healing mechanisms.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".