16,208 research outputs found
DeepMB: Deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound
Multispectral optoacoustic tomography (MSOT) is a high-resolution functional
imaging modality that can non-invasively access a broad range of
pathophysiological phenomena by quantifying the contrast of endogenous
chromophores in tissue. Real-time imaging is imperative to translate MSOT into
clinical imaging, visualize dynamic pathophysiological changes associated with
disease progression, and enable in situ diagnoses. Model-based reconstruction
affords state-of-the-art optoacoustic images; however, the image quality
provided by model-based reconstruction remains inaccessible during real-time
imaging because the algorithm is iterative and computationally demanding. Deep
learning affords faster reconstruction, but the lack of ground truth training
data can lead to reduced image quality for in vivo data. We introduce a
framework, termed DeepMB, that achieves accurate optoacoustic image
reconstruction for arbitrary input data in 31 ms per image by expressing
model-based reconstruction with a deep neural network. DeepMB facilitates
accurate generalization to experimental test data through training on signals
synthesized from real-world images and ground truth images generated by
model-based reconstruction. The framework affords in-focus images for a broad
range of anatomical locations because it supports dynamic adjustment of the
reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible
with the data rates and image sizes of modern multispectral optoacoustic
tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images
and demonstrate that the framework reconstructs images 1000 times faster than
the iterative model-based reference method while affording near-identical image
qualities. Accurate and real-time image reconstructions with DeepMB can enable
full access to the high-resolution and multispectral contrast of handheld
optoacoustic tomography
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Advanced optical imaging methods for investigating manuscripts
This paper gives an overview of advanced optical imaging methods relevant to the study of manuscripts. While some of the methods covered are well established, others are very much in active development. ‘Optical’ in this context is loosely defined to cover the near ultraviolet, visible and the near infrared part of the electromagnetic spectrum. Optical imaging methods are in general non-destructive and can be applied in situ. They are non-invasive if care is taken to ensure a safe dosage of illumination during the imaging process. The examples given in this paper are biased towards work that the author has been involved in. This is by no means a comprehensive review. The aim of the paper is to illustrate how advanced optical imaging techniques can assist in the investigation of manuscripts
Simultaneous exoplanet detection and instrument aberration retrieval in multispectral coronagraphic imaging
High-contrast imaging for the detection and characterization of exoplanets
relies on the instrument's capability to block out the light of the host star.
Some current post-processing methods for calibrating out the residual speckles
use information redundancy offered by multispectral imaging but do not use any
prior information on the origin of these speckles. We investigate whether
additional information on the system and image formation process can be used to
more finely exploit the multispectral information. We developed an inversion
method in a Bayesian framework that is based on an analytical imaging model to
estimate both the speckles and the object map. The model links the instrumental
aberrations to the speckle pattern in the image focal plane, distinguishing
between aberrations upstream and downstream of the coronagraph. We propose and
validate several numerical techniques to handle the difficult minimization
problems of phase retrieval and achieve a contrast of 10^6 at 0.2 arcsec from
simulated images, in the presence of photon noise. This opens up the the
possibility of tests on real data where the ultimate performance may override
the current techniques if the instrument has good and stable coronagraphic
imaging quality. This paves the way for new astrophysical exploitations or even
new designs for future instruments
SPLASSH: Open source software for camera-based high-speed, multispectral in-vivo optical image acquisition
Camera-based in-vivo optical imaging can provide detailed images of living tissue that reveal structure, function, and disease. High-speed, high resolution imaging can reveal dynamic events such as changes in blood flow and responses to stimulation. Despite these benefits, commercially available scientific cameras rarely include software that is suitable for in-vivo imaging applications, making this highly versatile form of optical imaging challenging and time-consuming to implement. To address this issue, we have developed a novel, open-source software package to control high-speed, multispectral optical imaging systems. The software integrates a number of modular functions through a custom graphical user interface (GUI) and provides extensive control over a wide range of inexpensive IEEE 1394 Firewire cameras. Multispectral illumination can be incorporated through the use of off-the-shelf light emitting diodes which the software synchronizes to image acquisition via a programmed microcontroller, allowing arbitrary high-speed illumination sequences. The complete software suite is available for free download. Here we describe the software’s framework and provide details to guide users with development of this and similar software
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
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Spectral imaging in preclinical research and clinical pathology.
Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings
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