Multimodal Retinal Imaging
Multimodal Retinal Imaging - Related Videos
09:17
Using Retinal Imaging to Study Dementia
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Cited by 19 •
2017
The retina shares prominent similarities with the brain and thus represents a unique window to study vasculature and neuronal structure in the brain non-invasively. This protocol describes a method to study dementia using retinal imaging techniques. This method can potentially aid in diagnosis and risk assessment of dementia.
04:47
Multimodal Optical Imaging Platform for Studying Cellular Metabolism
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2025
Utilizing a multimodal platform combining label-free optical imaging modalities, we have developed a protocol for visualizing and quantifying cellular dynamics and metabolism. Through imaging via multiphoton fluorescence, second harmonic generation, and stimulated Raman scattering microscopy, we can generate a holistic overview of the cellular and molecular environment.
12:38
4D Multimodality Imaging of Citrobacter rodentium Infections in Mice
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Cited by 19 •
2013
Multi-modality imaging is a valuable approach for studying bacterial colonization in small animal models. This protocol outlines infection of mice with bioluminescent Citrobacter rodentium and the longitudinal monitoring of bacterial colonization using composite 3D diffuse light imaging tomography with μCT imaging to create a 4D movie of C. rodentium infection.
09:56
In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
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Cited by 7 •
2018
Here, we present the usefulness of longitudinal in vivo imaging in the follow-up of morphological changes of laser-induced choroidal neovascularization in mice.
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Cited by 3 •
2023
The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.
FAQs
Computational registration aligns datasets from different retinal imaging modalities by bringing corresponding anatomical and physiological features into a common spatial framework. Image fusion then combines these aligned views, allowing researchers to compare surface appearance, depth-resolved structure, and vascular behavior in one analysis. This alignment is especially valuable when measurements arise at different scales or encode different signals.
Each modality records a distinct retinal signal. Fundus photography uses reflected light to map surface appearance, OCT applies low-coherence interferometry to resolve depth, and fluorescein angiography uses injected fluorescent dye to reveal vascular leakage and perfusion. Comparing these outputs helps distinguish structural findings from vascular or physiological features that a single imaging method may not show.
Cross-scale comparison connects visible retinal appearance with depth-resolved anatomy and vascular behavior. After registration and fusion, a researcher can examine whether features observed in one dataset correspond to findings in another, supporting quantitative biomarker development. This integrated perspective can also contribute to earlier disease detection and more precise evaluation of changes during treatment monitoring.
A typical workflow acquires complementary retinal datasets, including surface, depth, and vascular images, then computationally registers them so corresponding features align. Image fusion creates a combined representation for comparison and analysis. Researchers can subsequently extract quantitative features, evaluate anatomical and physiological relationships, and use the resulting datasets to support disease assessment or tool development.
Researchers may apply the approach when one imaging signal cannot adequately characterize the retinal finding under study. Its supported uses include earlier disease detection, quantitative biomarker evaluation, and treatment monitoring. By combining structural and vascular information, studies can compare anatomical and physiological changes more comprehensively than with an isolated imaging modality.
In bioengineering, aligned multimodal datasets provide a foundation for designing computational methods that relate retinal structure, blood flow, and function. Image registration and fusion make these signals more suitable for quantitative analysis and development of automated analysis tools. Such tools can assist ophthalmic research by processing complementary measurements within a coordinated framework rather than treating each image independently.