Light Field Volumetric Imaging for Scattering Brain Tissue
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Solution Overview
Problem
Capturing the activity of neuronal populations across multi-millimeter fields-of-view (FOVs) in the mammalian brain nearly-simultaneously and in a volumetric fashion has remained challenging due to limitations in existing imaging techniques, particularly in scattering brain tissues, which are based on sequential acquisition and lack efficient computational tools for high-resolution, mesoscopic volumetric recording.
Innovation Solution
MesoLF combines a modular hardware and software-based solution with mesoscale optical design and aberration correction, utilizing a scalable computational pipeline for neuronal localization and signal extraction, enabling volumetric recording of over 10,500 active neurons across different regions of the mouse cortex at 18 volumes per second, using a custom tube lens and microlens array with a phase-space-based reconstruction approach to correct for scattering and tissue morphology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sequential acquisition methods are used for imaging scattering brain tissues, then device complexity is reduced, but measurement precision and volumetric recording capability deteriorate
Solution Approach 1:
The patent transitions from sequential 2D imaging to simultaneous 3D volumetric imaging by introducing a light field microscopy approach with microlens arrays that capture spatial and angular information in multiple dimensions, enabling true volumetric recording without sequential scanning
Solution Approach 2:
The patent changes the imaging parameters by using widefield illumination and detection geometries with high numerical aperture objectives, transitioning from narrow focal planes to thick optical sections that capture volumetric information in a single shot
2Productivity
If light field microscopy is used to capture volumetric information, then productivity is improved, but manufacturing precision and resolution deteriorate due to scattering
Solution Approach 1:
The patent introduces computational algorithms as an intermediary between light field capture and final image reconstruction, using deconvolution and scattering correction methods to recover high-resolution volumetric information from the captured light field data
Solution Approach 2:
The patent replaces mechanical scanning systems with a computational imaging approach, substituting physical sequential acquisition with algorithmic processing of simultaneously captured light field data to achieve high resolution
3Measurement precision
If computational pipelines are added for neuronal localization and signal extraction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary computational steps during data acquisition, including real-time background subtraction and initial neuronal event detection, to reduce the complexity of subsequent offline analysis and enable precise localization
4Measurement precision
If aberration correction and scattering compensation are implemented, then measurement precision is improved, but use of energy and computational cost increase
Solution Approach 1:
The patent applies partial scattering correction by focusing computational resources on correcting the most significant aberrations and scattering effects in the focal plane and near field, rather than attempting complete correction throughout the entire volumetric range
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
Achieves high-resolution, mesoscopic volumetric imaging across multi-millimeter FOVs with reduced reconstruction artifacts and computational cost, allowing for accurate neuron localization and signal extraction, outperforming conventional methods in precision and sensitivity, and enabling long-duration recordings with workstation-grade resources.
Implementation Method 1
a camera is disposed in the focal plane of the microlenses to capture a raw image of the light field
Implementation Method 2
utilizing a scalable computational pipeline for neuronal localization and signal extraction, enabling volumetric recording... using a custom tube lens and microlens array
Data Source
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AI summary
A volumetric imaging system implements obtaining raw image data of a sample volume of a sample material, the raw image data comprising light field data of the sample volume acquired using a microlens array disposed in front of a camera; and analyzing the raw image data using an image analysis pipeline configured to localize the objects of interest in the raw image data to obtain classified image data in which the objects of interest have been identified, the image analysis pipeline being configured to process the raw image data to improve signal extraction at depth in the scattering material to maximize localization accuracy.