153,784 research outputs found
Recovering Dense Tissue Multispectral Signal from in vivo RGB Images
Hyperspectral/multispectral imaging (HSI/MSI) contains rich information
clinical applications, such as 1) narrow band imaging for vascular
visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and
clinical decision making [1]; 3) tissue classification and identification of
pathology [2]. The current systems which provide pixel-level HSI/MSI signal can
be generally divided into two types: spatial scanning and spectral scanning.
However, the trade-off between spatial/spectral resolution, the acquisition
time, and the hardware complexity hampers implementation in real-world
applications, especially intra-operatively. Acquiring high resolution images in
real-time is important for HSI/MSI in intra-operative imaging, to alleviate the
side effect caused by breathing, heartbeat, and other sources of motion.
Therefore, we developed an algorithm to recover a pixel-level MSI stack using
only the captured snapshot RGB images from a normal camera. We refer to this
technique as "super-spectral-resolution". The proposed method enables recovery
of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per
second (FPS) on a GPU. Multispectral data captured from porcine bowel and
sheep/rabbit uteri in vivo has been used for training, and the algorithm has
been validated using unseen in vivo animal experiments.Comment: accepted by Hamlyn Symposium 201
Infrared radiometric technique in temperature measurement
One class of commercially available imaging infrared radiometers using cooled detectors is sensitive to radiation over the 3 to 12 micron wavelength band. Spectral filters can tailor instrument sensitivity to specific regions where the target exhibits optimum radiance. The broadband spectral response coupled with real time two-dimensional imaging and emittance/background temperature corrections make the instruments useful for remote measurement of surface temperatures from -20 C to +1500 C. Commonly used radiometric techniques and assumptions are discussed, and performance specifications for a typical modern commercial instrument are presented. The potential usefulness of an imaging infrared radiometer in space laboratories is highlighted through examples of research, nondestructive evaluation, safety, and routine maintenance applications. Future improvements in instrument design and application of the radiometric technique are discussed
One-shot ultraspectral imaging with reconfigurable metasurfaces
One-shot spectral imaging that can obtain spectral information from thousands
of different points in space at one time has always been difficult to achieve.
Its realization makes it possible to get spatial real-time dynamic spectral
information, which is extremely important for both fundamental scientific
research and various practical applications. In this study, a one-shot
ultraspectral imaging device fitting thousands of micro-spectrometers (6336
pixels) on a chip no larger than 0.5 cm, is proposed and demonstrated.
Exotic light modulation is achieved by using a unique reconfigurable
metasurface supercell with 158400 metasurface units, which enables 6336
micro-spectrometers with dynamic image-adaptive performances to simultaneously
guarantee the density of spectral pixels and the quality of spectral
reconstruction. Additionally, by constructing a new algorithm based on
compressive sensing, the snapshot device can reconstruct ultraspectral imaging
information (/~0.001) covering a broad (300-nm-wide)
visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm
standard deviation and spectral resolution of 0.8 nm. This scheme of
reconfigurable metasurfaces makes the device can be directly extended to almost
any commercial camera with different spectral bands to seamlessly switch the
information between image and spectral image, and will open up a new space for
the application of spectral analysis combining with image recognition and
intellisense
A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging
Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique requires sequential measurements resulting in a trade-off between spatial resolution and acquisition time, limiting real-time video applications to relatively low resolutions. Compressed sensing techniques can be used to improve this trade-off. However, in this low resolution regime, conventional compressed sensing techniques have limited impact due to lack of sparsity in the datasets. Here we present an alternative compressed sensing method in which we optimize the measurement order of the Hadamard basis, such that at discretized increments we obtain complete sampling for different spatial resolutions. In addition, this method uses deterministic acquisition, rather than the randomized sampling used in conventional compressed sensing. This so-called ‘Russian Dolls’ ordering also benefits from minimal computational overhead for image reconstruction. We find that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction. Therefore, the proposed method may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition
Snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS)
We present a snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS) for eye imaging applications. The resulting system is capable of simultaneously acquiring 48 spectral channel images in the range 470 nm–650 nm with frame rate at 5.2 fps. The spatial sampling of each measured spectral scene is 350 × 350 pixels. The advantages of this snapshot device are elimination of the eye motion artifacts and pixel misregistration problems in traditional scanning-based hyperspectral retinal cameras, and real-time imaging of oxygen saturation dynamics with sub-second temporal resolution. The spectral imaging performance is demonstrated in a human retinal imaging experiment in vivo. The absorption spectral signatures of oxy-hemoglobin and macular pigments were successfully acquired by using this device
Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans.
Optoacoustic (OA) imaging is a promising modality for quantifying blood oxygen saturation (sO2) in various biomedical applications - in diagnosis, monitoring of organ function, or even tumor treatment planning. We present an accurate and practically feasible real-time capable method for quantitative imaging of sO2 based on combining multispectral (MS) and multiple illumination (MI) OA imaging with learned spectral decoloring (LSD). For this purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound (US) imaging capability; we trained gradient boosting machines on MI spectrally colored absorbed energy spectra generated by generic Monte Carlo simulations and used the trained models to estimate sO2 on real OA measurements. We validated MI-LSD in silico and on in vivo image sequences of radial arteries and accompanying veins of five healthy human volunteers. We compared the performance of the method to prior LSD work and conventional linear unmixing. MI-LSD provided highly accurate results in silico and consistently plausible results in vivo. This preliminary study shows a potentially high applicability of quantitative OA oximetry imaging, using our method
Analysis of Dynamic Brain Imaging Data
Modern imaging techniques for probing brain function, including functional
Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging,
and magnetoencephalography, generate large data sets with complex content. In
this paper we develop appropriate techniques of analysis and visualization of
such imaging data, in order to separate the signal from the noise, as well as
to characterize the signal. The techniques developed fall into the general
category of multivariate time series analysis, and in particular we extensively
use the multitaper framework of spectral analysis. We develop specific
protocols for the analysis of fMRI, optical imaging and MEG data, and
illustrate the techniques by applications to real data sets generated by these
imaging modalities. In general, the analysis protocols involve two distinct
stages: `noise' characterization and suppression, and `signal' characterization
and visualization. An important general conclusion of our study is the utility
of a frequency-based representation, with short, moving analysis windows to
account for non-stationarity in the data. Of particular note are (a) the
development of a decomposition technique (`space-frequency singular value
decomposition') that is shown to be a useful means of characterizing the image
data, and (b) the development of an algorithm, based on multitaper methods, for
the removal of approximately periodic physiological artifacts arising from
cardiac and respiratory sources.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files.
Originally submitted to the neuro-sys archive which was never publicly
announced (was 9804003
Slow light for deep tissue imaging with ultrasound modulation
Slow light has been extensively studied for applications ranging from optical delay lines to single photon quantum storage. Here, we show that the time delay of slow-light significantly improves the performance of the narrowband spectral filters needed to optically detect ultrasound from deep inside highly scatteringtissue. We demonstrate this capability with a 9 cm thick tissue phantom, having 10 cm^(−1) reduced scattering coefficient, and achieve an unprecedented background-free signal. Based on the data, we project real time imaging at video rates in even thicker phantoms and possibly deep enough into real tissue for clinical applications like early cancer detection
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