Ophthalmic Imaging Montage Optimization
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Solution Overview
Problem
Current ophthalmic imaging systems face challenges in obtaining high-resolution data over large fields of view due to large acquisition times and huge data volumes, particularly in generating en face vasculature images of the retina, which requires high-density sampling points and longer scan times, making widespread screening in clinics inefficient.
Innovation Solution
The system and method for improved montaging of retinal images allow for optimized scan patterns based on the physical characteristics of the eye, varying overlap between constituent images, and applying artifact removal, with a preliminary scan to identify optimal overlap and scan sizes, and quality checks to ensure accurate placement and quality of the montaged images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density sampling points are used to generate en face vasculature images, then measurement precision is improved, but acquisition time increases significantly
Solution Approach 1:
The patent divides the large field of view into multiple smaller scan regions that are acquired separately and then montaged together. This segmentation allows the system to maintain high sampling density within each smaller region while reducing the total acquisition time compared to scanning the entire large area with the same density.
Solution Approach 2:
The patent uses overlapping scan regions where certain areas are scanned multiple times. This partial redundancy ensures that high-quality data is captured in the overlap zones for accurate image registration and montage, while accepting that some regions receive more scans than strictly necessary for coverage.
2Loss of time
If multiple smaller data cubes are scanned and montaged to generate large field of view images, then acquisition time is reduced, but device complexity increases
Solution Approach 1:
The patent performs preliminary registration and alignment of scan regions using reference features identified in the overlap zones before final montage. This preliminary action simplifies the overall processing by establishing coordinate transformations early, making the subsequent montage operation more straightforward and computationally efficient.
Solution Approach 2:
The patent uses overlap regions between adjacent scan cubes as intermediary zones for registration and alignment. These intermediary regions contain common features that facilitate accurate matching and transformation between different scan regions, simplifying the montage process.
3Manufacturing precision
If scan overlap is increased to improve image registration accuracy, then manufacturing precision is improved, but data volume and processing time increase
Solution Approach 1:
The patent applies different processing strategies to different regions: the overlap regions are used specifically for registration and alignment purposes, while the non-overlap regions provide the unique field of view coverage. This local quality approach optimizes the use of data in each region for its specific purpose.
Solution Approach 2:
The patent uses a moderate amount of overlap between scan regions - sufficient to provide adequate reference features for registration, but not so much as to create excessive redundant data. This partial action balances registration accuracy with data efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces patient imaging time, enhances the quality of montaged images by optimizing scan patterns and image overlap, and improves the efficiency of data acquisition, allowing for more comprehensive and accurate retinal imaging without the need for invasive techniques.
Implementation Method 1
OCT is based on the principle of low coherence interferometry (LCI) and determines the scattering profile of a sample along the OCT beam by detecting the interference of light reflected from a sample and a reference beam
Implementation Method 2
Optical coherence tomography (OCT) is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue microstructure
Implementation Method 3
There are several flow contrast techniques in OCT imaging that utilize the change in data between successive B-scans or frames (inter-frame change analysis) of the OCT intensity or phase-resolved OCT data
Data Source
AI summary
An ophthalmic imaging system provides a user interface to facilitate the montaging of scan images collected with various imaging modalities, such as images collected with a fundus imaging system or an optical coherence tomography (OCT) system. The amount of each constituent image used in the montage is dependent upon its respective quality. During the collecting of scans (constituent images) for montaging, any scan may be designated for rescanning, such as if its current quality is deemed less than sufficient. In the case of using an OCT system to collect constituent images (e.g., cube scans), the scanned region of a constituent image may be modified based on physical characteristics of the eye being scanned.