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: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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