Dataset Information

High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging.

ABSTRACT: Progress in neuroscience relies on new techniques for investigating the complex dynamics of neuronal networks. An ongoing challenge is to achieve minimally invasive and high-resolution observations of neuronal activity in vivo inside deep brain areas. Recently introduced methods for holographic control of light propagation in complex media enable the use of a hair-thin multimode optical fibre as an ultranarrow imaging tool. Compared to endoscopes based on graded-index lenses or fibre bundles, this new approach offers a footprint reduction exceeding an order of magnitude, combined with a significant enhancement in resolution. We designed a compact and high-speed system for fluorescent imaging at the tip of a fibre, achieving a resolution of 1.18?±?0.04?µm across a 50-µm field of view, yielding 7-kilopixel images at a rate of 3.5 frames/s. Furthermore, we demonstrate in vivo observations of cell bodies and processes of inhibitory neurons within deep layers of the visual cortex and hippocampus of anaesthetised mice. This study paves the way for modern microscopy to be applied deep inside tissues of living animal models while exerting a minimal impact on their structural and functional properties.

SUBMITTER: Turtaev S

PROVIDER: S-EPMC6249210 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging.

Turtaev Sergey S Leite Ivo T IT Altwegg-Boussac Tristan T Pakan Janelle M P JMP Rochefort Nathalie L NL Čižmár Tomáš T

Light, science & applications 20181121

Progress in neuroscience relies on new techniques for investigating the complex dynamics of neuronal networks. An ongoing challenge is to achieve minimally invasive and high-resolution observations of neuronal activity in vivo inside deep brain areas. Recently introduced methods for holographic control of light propagation in complex media enable the use of a hair-thin multimode optical fibre as an ultranarrow imaging tool. Compared to endoscopes based on graded-index lenses or fibre bundles, th ...[more]

PMID: 30479758

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Project description:SignificanceHair-thin multimode optical fiber-based holographic endoscopes have gained considerable interest in modern neuroscience for their ability to achieve cellular and even subcellular resolution during in-vivo deep brain imaging. However, the application of multimode fibers in freely moving animals presents a persistent challenge as it is difficult to maintain optimal imaging performance while the fiber undergoes deformations.AimWe propose a fiber solution for challenging in-vivo applications with the capability of deep brain high spatial resolution imaging and neuronal activity monitoring in anesthetized as well as awake behaving mice.ApproachWe used our previously developed M3CF multimode-multicore fiber to record fluorescently labeled neurons in anesthetized mice. Our M3CF exhibits a cascaded refractive index structure, enabling two distinct regimes of light transport that imitate either a multimode or a multicore fiber. The M3CF has been specifically designed for use in the initial phase of an in-vivo experiment, allowing for the navigation of the endoscope's distal end toward the targeted brain structure. The multicore regime enables the transfer of light to and from each individual neuron within the field of view. For chronic experiments in awake behaving mice, it is crucial to allow for disconnecting the fiber and the animal between experiments. Therefore, we provide here an effective solution and establish a protocol for reconnection of two segments of M3CF with hexagonally arranged corelets.ResultsWe successfully utilized the M3CF to image neurons in anaesthetized transgenic mice expressing enhanced green fluorescent protein. Additionally, we compared imaging results obtained with the M3CF with larger numerical aperture (NA) fibers in fixed whole-brain tissue.ConclusionsThis study focuses on addressing challenges and providing insights into the use of multimode-multicore fibers as imaging solutions for in-vivo applications. We suggest that the upcoming version of the M3CF increases the overall NA between the two cladding layers to allow for access to high resolution spatial imaging. As the NA increases in the multimode regime, the fiber diameter and ring structure must be reduced to minimize the computational burden and invasiveness.

Project description:Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b = 0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b = 0 images and 21 DWIs for the primary eigenvector derived from DTI and two b = 0 images and 26-30 DWIs for various scalar metrics derived from DTI, achieving 3.3-4.6 × acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 b = 0 images and 90 DWIs measures around 1-1.5 mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

Dataset Information

High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging.

Publications

High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging.

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