46,091 research outputs found
Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall
SIGNIFICANCE: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue.
AIM: We aim to reduce the chest wall\u27s effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction.
APPROACH: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall.
RESULTS: The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth.
CONCLUSIONS: Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties
Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation
In this paper a new optical-computational method is introduced to unveil
images of targets whose visibility is severely obscured by light scattering in
dense, turbid media. The targets of interest are taken to be dynamic in that
their optical properties are time-varying whether stationary in space or
moving. The scheme, to our knowledge the first of its kind, is human vision
inspired whereby diffuse photons collected from the turbid medium are first
transformed to spike trains by a dynamic vision sensor as in the retina, and
image reconstruction is then performed by a neuromorphic computing approach
mimicking the brain. We combine benchtop experimental data in both reflection
(backscattering) and transmission geometries with support from physics-based
simulations to develop a neuromorphic computational model and then apply this
for image reconstruction of different MNIST characters and image sets by a
dedicated deep spiking neural network algorithm. Image reconstruction is
achieved under conditions of turbidity where an original image is
unintelligible to the human eye or a digital video camera, yet clearly and
quantifiable identifiable when using the new neuromorphic computational
approach
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical
imaging modality that can rapidly provide volumetric images of samples. Here,
we present a deep learning-based image reconstruction framework that can
generate swept-source OCT (SS-OCT) images using undersampled spectral data,
without any spatial aliasing artifacts. This neural network-based image
reconstruction does not require any hardware changes to the optical set-up and
can be easily integrated with existing swept-source or spectral domain OCT
systems to reduce the amount of raw spectral data to be acquired. To show the
efficacy of this framework, we trained and blindly tested a deep neural network
using mouse embryo samples imaged by an SS-OCT system. Using 2-fold
undersampled spectral data (i.e., 640 spectral points per A-line), the trained
neural network can blindly reconstruct 512 A-lines in ~6.73 ms using a desktop
computer, removing spatial aliasing artifacts due to spectral undersampling,
also presenting a very good match to the images of the same samples,
reconstructed using the full spectral OCT data (i.e., 1280 spectral points per
A-line). We also successfully demonstrate that this framework can be further
extended to process 3x undersampled spectral data per A-line, with some
performance degradation in the reconstructed image quality compared to 2x
spectral undersampling. This deep learning-enabled image reconstruction
approach can be broadly used in various forms of spectral domain OCT systems,
helping to increase their imaging speed without sacrificing image resolution
and signal-to-noise ratio.Comment: 20 Pages, 7 Figures, 1 Tabl
Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images
With the increasing availability of optical and synthetic aperture radar
(SAR) images thanks to the Sentinel constellation, and the explosion of deep
learning, new methods have emerged in recent years to tackle the reconstruction
of optical images that are impacted by clouds. In this paper, we focus on the
evaluation of convolutional neural networks that use jointly SAR and optical
images to retrieve the missing contents in one single polluted optical image.
We propose a simple framework that ease the creation of datasets for the
training of deep nets targeting optical image reconstruction, and for the
validation of machine learning based or deterministic approaches. These methods
are quite different in terms of input images constraints, and comparing them is
a problematic task not addressed in the literature. We show how space
partitioning data structures help to query samples in terms of cloud coverage,
relative acquisition date, pixel validity and relative proximity between SAR
and optical images. We generate several datasets to compare the reconstructed
images from networks that use a single pair of SAR and optical image, versus
networks that use multiple pairs, and a traditional deterministic approach
performing interpolation in temporal domain.Comment: 17 page
Range-Point Migration-Based Image Expansion Method Exploiting Fully Polarimetric Data for UWB Short-Range Radar
Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria
Spatio-temporal reconstruction of drop impact dynamics by means of color-coded glare points and deep learning
The present work introduces a deep learning approach for the
three-dimensional reconstruction of the spatio-temporal dynamics of the
gas-liquid interface in two-phase flows on the basis of monocular images
obtained via optical measurement techniques. The dynamics of liquid droplets
impacting onto structured solid substrates are captured through high-speed
imaging in an extended shadowgraphy setup with additional reflective glare
points from lateral light sources that encode further three-dimensional
information of the gas-liquid interface in the images. A neural network is
learned for the physically correct reconstruction of the droplet dynamics on a
labelled dataset generated by synthetic image rendering on the basis of
gas-liquid interface shapes obtained from direct numerical simulation. The
employment of synthetic image rendering allows for the efficient generation of
training data and circumvents the introduction of errors resulting from the
inherent discrepancy of the droplet shapes between experiment and simulation.
The accurate reconstruction of the gas-liquid interface during droplet
impingement on the basis of images obtained in the experiment demonstrates the
practicality of the presented approach based on neural networks and synthetic
training data generation. The introduction of glare points from lateral light
sources in the experiments is shown to improve the reconstruction accuracy,
which indicates that the neural network learns to leverage the additional
three-dimensional information encoded in the images for a more accurate depth
estimation. Furthermore, the physically reasonable reconstruction of unknown
gas-liquid interface shapes indicates that the neural network learned a
versatile model of the involved two-phase flow phenomena during droplet
impingement
Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation. It was shown that convolutional neural networks (CNNs) could improve pCLE image quality. Yet classical CNNs may be suboptimal in regard to irregular data. METHODS: We compare pCLE reconstruction and super-resolution (SR) methods taking irregularly sampled or reconstructed pCLE images as input. We also propose to embed a Nadaraya-Watson (NW) kernel regression into the CNN framework as a novel trainable CNN layer. We design deep learning architectures allowing for reconstructing high-quality pCLE images directly from the irregularly sampled input data. We created synthetic sparse pCLE images to evaluate our methodology. RESULTS: The results were validated through an image quality assessment based on a combination of the following metrics: peak signal-to-noise ratio and the structural similarity index. Our analysis indicates that both dense and sparse CNNs outperform the reconstruction method currently used in the clinic. CONCLUSION: The main contributions of our study are a comparison of sparse and dense approach in pCLE image reconstruction. We also implement trainable generalised NW kernel regression as a novel sparse approach. We also generated synthetic data for training pCLE SR
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