Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning
Guiqiu Liao, Oscar Caravaca-Mora, Benoit Rosa, Philippe Zanne,, Alexandre Asch, Diego Dall Alba, Paolo Fiorini, Michel de Mathelin, Florent, Nageotte, Michalina J. Gora

TL;DR
This paper introduces a CNN-based volumetric data stabilization algorithm for OCT imaging that estimates and corrects rotational distortions caused by variable catheter rotation speeds, improving image quality.
Contribution
The novel stabilization algorithm estimates Non-Uniform Rotational Distortion using CNNs and addresses accumulative errors, applicable in different OCT scanning modes.
Findings
Effective correction of rotational distortion in synthetic and real OCT scans.
Robust performance across different scanning modes.
Enhanced image stability and quality in OCT volumetric imaging.
Abstract
Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between two successive B-scans is computed and provided as input to the CNN. To solve the problem of accumulative error in iterative frame stream processing, we deploy an overall rotation estimation between reference orientation and actual OCT image orientation. We train the network with synthetic OCT…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
