Visible spectrum extended-focus optical coherence microscopy for label-free sub-cellular tomography

We present a novel extended-focus optical coherence microscope (OCM) attaining 0.7 {\mu}m axial and 0.4 {\mu}m lateral resolution maintained over a depth of 40 {\mu}m, while preserving the advantages of Fourier domain OCM. Our method uses an ultra-broad spectrum from a super- continuum laser source. As the spectrum spans from near-infrared to visible wavelengths (240 nm in bandwidth), we call the method visOCM. The combination of such a broad spectrum with a high-NA objective creates an almost isotropic 3D submicron resolution. We analyze the imaging performance of visOCM on microbead samples and demonstrate its image quality on cell cultures and ex-vivo mouse brain tissue.Comment: 15 pages, 7 figure

Optical imaging of the small intestine immune compartment across scales.

The limitations of 2D microscopy constrain our ability to observe and understand tissue-wide networks that are, by nature, 3-dimensional. Optical projection tomography (OPT) enables the acquisition of large volumes (ranging from micrometres to centimetres) in various tissues. We present a multi-modal workflow for the characterization of both structural and quantitative parameters of the mouse small intestine. As proof of principle, we evidence its applicability for imaging the mouse intestinal immune compartment and surrounding mucosal structures. We quantify the volumetric size and spatial distribution of Isolated Lymphoid Follicles (ILFs) and quantify the density of villi throughout centimetre-long segments of intestine. Furthermore, we exhibit the age and microbiota dependence for ILF development, and leverage a technique that we call reverse-OPT for identifying and homing in on regions of interest. Several quantification capabilities are displayed, including villous density in the autofluorescent channel and the size and spatial distribution of the signal of interest at millimetre-scale volumes. The concatenation of 3D imaging with reverse-OPT and high-resolution 2D imaging allows accurate localisation of ROIs and adds value to interpretations made in 3D. Importantly, OPT may be used to identify sparsely-distributed regions of interest in large volumes whilst retaining compatibility with high-resolution microscopy modalities, including confocal microscopy. We believe this pipeline to be approachable for a wide-range of specialties, and to provide a new method for characterisation of the mouse intestinal immune compartment

Statistical parametric mapping of stimuli evoked changes in total blood flow velocity in the mouse cortex obtained with extended-focus optical coherence microscopy

Functional magnetic resonance (fMRI) imaging is the current gold-standard in neuroimaging. fMRI exploits local changes in blood oxygenation to map neuronal activity over the entire brain. However, its spatial resolution is currently limited to a few hundreds of microns. Here we use extended-focus optical coherence microscopy (xfOCM) to quantitatively measure changes in blood flow velocity during functional hyperaemia at high spatio-temporal resolution in the somatosensory cortex of mice. As optical coherence microscopy acquires hundreds of depth slices simultaneously, blood flow velocity measurements can be performed over several vessels in parallel. We present the proof-of-principle of an optimised statistical parametric mapping framework to analyse quantitative blood flow timetraces acquired with xfOCM using the general linear model. We demonstrate the feasibility of generating maps of cortical hemodynamic reactivity at the capillary level with optical coherence microscopy. To validate our method, we exploited 3 stimulation paradigms, covering different temporal dynamics and stimulated limbs, and demonstrated its repeatability over 2 trials, separated by a week

Statistical parametric mapping of stimuli evoked changes in total blood flow velocity in the mouse cortex obtained with extended-focus optical coherence microscopy

Functional magnetic resonance (fMRI) imaging is the current gold-standard in neuroimaging. fMRI exploits local changes in blood oxygenation to map neuronal activity over the entire brain. However, its spatial resolution is currently limited to a few hundreds of microns. Here we use extended-focus optical coherence microscopy (xfOCM) to quantitatively measure changes in blood flow velocity during functional hyperaemia at high spatio-temporal resolution in the somatosensory cortex of mice. As optical coherence microscopy acquires hundreds of depth slices simultaneously, blood flow velocity measurements can be performed over several vessels in parallel. We present the proof-of-principle of an optimised statistical parametric mapping framework to analyse quantitative blood flow timetraces acquired with xfOCM using the general linear model. We demonstrate the feasibility of generating maps of cortical hemodynamic reactivity at the capillary level with optical coherence microscopy. To validate our method, we exploited 3 stimulation paradigms, covering different temporal dynamics and stimulated limbs, and demonstrated its repeatability over 2 trials, separated by a week

Data models for dataset drift controls in machine learning with optical images

Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important public services spanning medicine or environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of machine learning’s primary object of interest: the data. This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner. In this study, we demonstrate how to overcome this limitation by pairing traditional machine learning with physical optics to obtain explicit and differentiable data models. We demonstrate how such data models can be constructed for image data and used to control downstream machine learning model performance related to dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases to power model selection and targeted generalization. Second, the gradient connection between machine learning task model and data model allows advanced, precise tolerancing of task model sensitivity to changes in the data generation. These drift forensics can be used to precisely specify the acceptable data environments in which a task model may be run. Third, drift optimization opens up the possibility to create drifts that can help the task model learn better faster, effectively optimizing the data generating process itself to support the downstream machine vision task. This is an interesting upgrade to existing imaging pipelines which traditionally have been optimized to be consumed by human users but not machine learning models. The data models require access to raw sensor images as commonly processed at scale in industry domains such as microscopy, biomedicine, autonomous vehicles or remote sensing. Alongside the data model code we release two datasets to the public that we collected as part of this work. In total, the two datasets, Raw-Microscopy and Raw-Drone, comprise 1,488 scientifically calibrated reference raw sensor measurements, 8,928 raw intensity variations as well as 17,856 images processed through twelve data models with different configurations. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit

Optical projection tomography for rapid whole mouse brain imaging

In recent years, three-dimensional mesoscopic imaging has gained significant importance in life sciences for fundamental studies at the whole-organ level. In this manuscript, we present an optical projection tomography (OPT) method designed for imaging of the intact mouse brain. The system features an isotropic resolution of ~50 μm and an acquisition time of four to eight minutes, using a 3-day optimized clearing protocol. Imaging of the brain autofluorescence in 3D reveals details of the neuroanatomy, while the use of fluorescent labels displays the vascular network and amyloid deposition in 5xFAD mice, an important model of Alzheimer’s disease (AD). Finally, the OPT images are compared with histological slices

Nonlinear bio-imaging and detection with ultrafast lasers

Après une introduction théorique (Chapitre 1) et le passage en revue du matériel expérimental utilisé (Chapitre 2), les différentes expériences sont regroupées en trois chapitres distincts selon leur thématique. Tout d'abord, l'utilisation de nano-cristaux non-linéaires comme marqueurs pour la microscopie est démontrée (Chapitre 3). Puis, le développement d'un nouveau façonneur d'impulsions laser et l'application d'un nouveau algorithme aux problèmes de contrôle optimal sont décrits (Chapitre 4). Pour finir, la question de la conservation de la phase des impulsions laser lors de leur propagation au travers d'une atmosphère turbulente est abordée (Chapitre 5)

Effects of atmospheric turbulence on remote optimal control experiments

Distortions of ultrashort laser pulses propagating through turbulence are investigated both experimentally and numerically. As expected, a strong correlation is found between temporal distortions and local intensity on the speckle pattern. We suggest that the localization of distortions in low-intensity regions may favor remote control strategies based on nonlinear interactions with respect to those based on linear schemes

Distributed temperature sensor combining centimeter resolution with hundreds of meters sensing range

We present a Raman distributed temperature sensor based on standard telecom single mode fibers and efficient polarization-independent superconducting nanowire single photon detectors. Our device shows 3 cm and 1.5 °C resolution on a 5 m fiber upon one minute integration. We show that spatial resolution is limited by the laser pulse width and not by the detection system. Moreover, for long fibers the minimum distance for a measurable temperature step change increases of around 4 cm per km length, because of chromatic dispersion at the Stokes and Anti-Stokes wavelengths. Temperature resolution is mainly affected by the drop in the laser repetition rate when long fibers are tested. On a 500 m fiber, a trade-off of 10 cm and 8 °C resolution is achieved with 3 minutes integration. Fiber-based distributed temperature sensing, combining centimetric spatial resolution with hundreds of meters sensing range, could pave the way for a new kind of applications, such as 2D and 3D temperature mapping of complex electronic devices, particles detectors, cryogenic and aerospace instrumentation