Combined OCT-Visualization Calibration for Accurate Image Alignment
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Solution Overview
Problem
Existing imaging systems face challenges in aligning optical coherence tomography (OCT) images with other modalities, particularly due to complex mechanical scanning and the time-consuming nature of manual and semi-automatic calibration methods.
Innovation Solution
An imaging system with a controller that generates a scanning pattern, synchronously acquires OCT data, and performs cascaded image registration to align OCT and visualization data using affine diffusion tensor image registration, minimizing manual intervention and environmental mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used to align OCT and visualization modalities, then alignment accuracy can be achieved, but the process becomes time-consuming and requires experienced technical support staff
Solution Approach 1:
The system performs preliminary alignment using mechanical scanning parameters before the actual calibration process, pre-positioning the OCT and visualization modalities in approximate alignment. This preliminary action reduces the complexity and time of the subsequent calibration procedure while maintaining accuracy.
Solution Approach 2:
A calibration target with known geometric features serves as an intermediary object between the OCT and visualization modalities. The target provides reference points that facilitate automated feature detection and alignment calculation, eliminating the need for manual alignment by experienced staff while maintaining high precision.
2Extent of automation
If semi-automatic calibration is used, then some automation is achieved, but the process remains tedious and complex
Solution Approach 1:
The system performs self-calibration by automatically detecting features in the calibration target from both OCT and visualization data, computing transformation parameters, and applying alignment corrections without user intervention. This self-service approach maximizes automation while the standardized calibration target keeps the process simple.
Solution Approach 2:
The patent replaces manual mechanical adjustment procedures with automated computational methods. Instead of physically adjusting components based on operator skill, the system uses image processing algorithms and mathematical transformations to achieve alignment automatically, reducing both manual complexity and procedural steps.
3Adaptability or versatility
If mechanical scanning is used in point-scanning OCT systems, then imaging capability is maintained, but alignment with other modalities becomes complex
Solution Approach 1:
The calibration target acts as a mediator that translates between the coordinate systems of the mechanically scanned OCT system and the visualization modality. By providing known reference features, it simplifies the complex transformation required between different scanning geometries and imaging modalities.
Solution Approach 2:
The system transforms mechanical scanning parameters into image coordinate parameters through calibration. By changing the representation from physical scanning positions to digital image coordinates, the complex mechanical alignment problem is converted into a simpler computational coordinate transformation.
Data Source
AI summary
An imaging system includes a housing assembly having a head unit configured to be at least partially directed towards a target site. An optical coherence tomography (OCT) module and a visualization module are located in the housing assembly and configured to respectively obtain OCT data and visualization data of the target site. The system includes a controller configured to generate a scanning pattern for a region of calibration selected in a calibration target. OCT data of the region of calibration is synchronously acquired. The controller is configured to obtain a projected two-dimensional OCT image of the region of calibration based on the OCT data, as an inverse mean-intensity projection. The controller is configured to register the projected two-dimensional OCT image to a corresponding view extracted from the visualization data, via a cascaded image registration process having a coarse registration stage and a fine registration stage.