Retinal Cell Optical Imaging for Quantifying Light-Evoked Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for imaging the structure and response of the retina lack high spatial and temporal resolution and good signal-to-noise ratio, hindering effective identification and treatment of retinal diseases.
Innovation Solution
A method involving capturing multiple images of retinal cells under different light conditions to induce physiological responses, followed by image processing to quantify these responses, using an optical instrument with a light source, beam splitter, and image sensor to achieve high spatiotemporal resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging techniques are used for the retina, then the imaging process is simple, but the spatial resolution, temporal resolution, and signal-to-noise ratio are insufficient
Solution Approach 1:
The imaging system segments the retina into multiple focal planes using adaptive optics and optical sectioning techniques. This allows high-resolution imaging of specific retinal layers while maintaining a manageable system complexity through targeted observation rather than attempting to capture the entire retinal volume simultaneously.
Solution Approach 2:
The patent introduces temporal dimension through rapid sequential imaging at different focal planes and uses optical coherence tomography to add depth dimension. This multi-dimensional approach achieves high spatial resolution in three dimensions while the rapid acquisition maintains high temporal resolution, resolving the contradiction between measurement precision and device complexity.
2Measurement precision
If conventional imaging techniques are used for the retina, then the imaging process is simple, but the temporal resolution is insufficient
Solution Approach 1:
The system uses periodic scanning patterns to rapidly acquire images at different retinal depths and locations. By implementing systematic raster scanning or focal plane sequencing with high repetition rates, the system achieves high temporal resolution through periodic sampling while maintaining controlled complexity through automated scanning protocols.
Solution Approach 2:
The imaging system performs preliminary focusing and alignment using adaptive optics before the main imaging sequence. This preliminary action optimizes the optical path in advance, allowing subsequent rapid image acquisition with high temporal resolution without requiring complex real-time adjustments during the imaging sequence.
3Measurement precision
If conventional imaging techniques are used for the retina, then the signal-to-noise ratio is poor, but the imaging setup is simpler
Solution Approach 1:
The system merges multiple low-signal images acquired at different focal planes and wavelengths, then applies coherent integration and signal processing algorithms. This combining approach enhances the signal-to-noise ratio by accumulating signal while random noise averages out, achieving high measurement precision without requiring excessively complex single-shot imaging hardware.
Solution Approach 2:
The imaging system incorporates feedback through adaptive optics that continuously measure and correct optical aberrations in real-time. This feedback loop maintains optimal focus and minimizes signal degradation, significantly improving signal-to-noise ratio while the automated feedback control keeps system complexity manageable through closed-loop stabilization.
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
Enables non-invasive assessment of retinal function at cellular resolution, allowing for accurate diagnosis and treatment of retinal disorders by measuring nanometer-scale shape and refractive index changes in response to light stimuli.
Implementation Method 1
capturing one or more first images of retinal cells of an eye; illuminating the retinal cells with a first light after capturing the one or more first images, to cause the retinal cells to exhibit a first physiological response
Data Source
AI summary
A method includes capturing one or more first images of retinal cells of an eye and illuminating the retinal cells with a first light after capturing the one or more first images, to cause the retinal cells to exhibit a first response. The method also includes capturing one or more second images of the retinal cells after illuminating the retinal cells with the first light and illuminating the retinal cells with a second light after capturing the one or more second images, to cause the retinal cells to exhibit a second response. The method also includes capturing one or more third images of the retinal cells after illuminating the retinal cells with the second light. The method also includes generating an output, using the first images, the second images, and the third images. The output quantifies the first response and the second response.