OCT Cornea Imaging Motion Correction via Sparse Dense Scans
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Solution Overview
Problem
Current optical coherence tomography (OCT) methods for ophthalmic applications, such as pachymetry and keratometry, face challenges due to motion-related errors during data acquisition, leading to inaccuracies in corneal thickness and curvature measurements, especially in abnormal corneas or after refractive surgery.
Innovation Solution
A method involving sparse and dense scan patterns, eye tracking mechanisms, and advanced motion correction algorithms to create accurate corneal surface models, allowing for enhanced pachymetry maps, keratometric values, and corneal power calculations by accounting for eye motion and improving segmentation and fitting techniques using Random Sample Consensus (RANSAC) and Zernike polynomials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If denser sampling is used to generate high density pachymetry maps, then measurement precision is improved, but scan time increases making data more susceptible to eye motion
Solution Approach 1:
The system performs preliminary eye motion tracking and correction during the scan acquisition process. By continuously monitoring eye position and applying motion correction algorithms in real-time, the system can use longer scan times for denser sampling without suffering from motion artifacts, thus resolving the contradiction between measurement precision and scan time.
2Measurement precision
If more meridional scans are acquired to minimize probability of missing pathology, then measurement precision is improved, but scan time increases making data more susceptible to eye motion
Solution Approach 1:
The system implements feedback through continuous eye tracking that monitors eye position and motion throughout the scanning process. This feedback information is used to dynamically adjust and correct the scan data, allowing multiple meridional scans to be acquired with motion compensation, thereby improving pathology detection accuracy without proportionally increasing the negative impact of scan time.
3Device complexity
If standard OCT segmentation and fitting techniques are used, then device complexity is kept low, but measurement precision deteriorates in abnormal corneas or after refractive surgery
Solution Approach 1:
The system dynamically adjusts processing parameters based on the detected corneal condition. For abnormal corneas or post-refractive surgery cases, the system modifies segmentation and fitting algorithm parameters to account for irregular corneal geometries, thereby improving measurement precision without requiring a complete overhaul of the device complexity.
Data Source
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AI summary
Systems and methods for enhanced accuracy in optical coherence tomography imaging of the cornea are presented, including approaches for more accurate corneal surface modeling, pachymetry maps, keratometric values, and corneal power. These methods involve new scan patterns, an eye tracking mechanism for transverse motion feedback, and advanced motion correction algorithms. In one embodiment the methods comprise acquiring a first sparse set of data, using that data to create a corneal surface model, and then using the model to register a second set of denser data acquisition. This second set of data is used to create a more accurate, motion-corrected model of the cornea, from which pachymetry maps, keratometric values, and corneal power information can be generated. In addition, methods are presented for determining simulated keratometry values from optical coherence tomography data, and for better tracking and registration by using both rotation about three axes and the corneal apex.