Dense spatial sampling is required in high-resolution optical imaging and many other biomedical optical imaging
methods, such as diffuse optical imaging. Arrayed photodetectors, in particular charge coupled device cameras are
commonly used mainly because of their high pixel count. Nonetheless, discrete-element photodetectors, such as
photomultiplier tubes, are often desirable in many performance-demanding imaging applications. However, utilization of
the discrete-element photodetectors typically requires raster scan to achieve arbitrary retrospective sampling with high
density. Care must be taken in using the relatively large sensitive areas of discrete-element photodetectors to densely
sample the image plane. In addition, off-line data analysis and image reconstruction often require full-field sampling.
Pixel-by-pixel scanning is not only slow but also unnecessary in diffusion-limited imaging. We propose a superresolution
method that can recover the finer features of an image sampled with a coarse-scale sensor. This generalpurpose
method was established on the spatial transfer function of the photodetector-lens system, and achieved superresolution
by inversion of this linear transfer function. Regularized optimization algorithms were used to achieve
optimized deconvolution. Compared to the uncorrected blurred image, the proposed super-resolution method
significantly improved image quality in terms of resolution and quantitation. Using this reconstruction method, the
acquisition speed with a scanning photodetector can be dramatically improved without significantly sacrificing sampling
density or flexibility.
Image formation in fluorescence diffuse optical tomography is critically dependent on construction of the Jacobian
matrix. For clinical and preclinical applications, because of the highly heterogeneous characteristics of the medium,
Monte Carlo methods are frequently adopted to construct the Jacobian. Conventional adjoint Monte Carlo method
typically compute the Jacobian by multiplying the photon density fields radiated from the source at the excitation
wavelength and from the detector at the emission wavelength. Nonetheless, this approach assumes that the source and
the detector in Green's function are reciprocal, which is invalid in general. This assumption is particularly questionable
in small animal imaging, where the mean free path length of photons is typically only one order of magnitude smaller
than the representative dimension of the medium. We propose a new method that does not rely on the reciprocity of the
source and the detector by tracing photon propagation entirely from the source to the detector. This method relies on the
perturbation Monte Carlo theory to account for the differences in optical properties of the medium at the excitation and
the emission wavelengths. Compared to the adjoint methods, the proposed method is more valid in reflecting the
physical process of photon transport in diffusive media and is more efficient in constructing the Jacobian matrix for
densely sampled configurations.
Quantum dots (QDs) are widely used in fluorescence tomography due to its unique advantages. Despite the very high
quantum efficiency of the QDs, low fluorescent signal and autofluorescence are the most fundamental limitations in
optical data acquisition. These limitations are particularly detrimental to image reconstruction for animal imaging, e.g.,
free-space in vivo fluorescence tomography. In animals studies, fluorescent emission from exogenous fluorescent probes
(e.g. QDs) cannot be effectively differentiated from endogenous broad-spectral substances (mostly proteins) using
optical filters. In addition, a barrow-band fluorescent filter blocks the majority of the fluorescent light and thus makes
signal acquisition very inefficient. We made use of the long fluorescent lifetime of the QDs to reject the optical signal
due to the excitation light pulse, and therefore eliminated the need for a fluorescent filter during acquisition. Fluorescent
emission from the QDs was excited with an ultrafast pulsed laser, and was detected using a time-gated image intensifier.
A tissue-simulating imaging phantom was used to validate the proposed method. Compared to the standard acquisition
method that uses a narrow-band fluorescent filter, the proposed method is significantly more efficient in data acquisition
(by a factor of >10 in terms of fluorescent signal intensity) and demonstrated reduction in autofluorescence. No
additional imaging artifact was observed in the tomographic reconstruction.
Image reconstruction is one of the main challenges for fluorescence tomography. For in vivo experiments on small
animals, in particular, the inhomogeneous optical properties and irregular surface of the animal make free-space image
reconstruction challenging because of the difficulties in accurately modeling the forward problem and the finite dynamic
range of the photodetector. These two factors are fundamentally limited by the currently available forward models and
photonic technologies. Nonetheless, both limitations can be significantly eased using a signal processing approach. We
have recently constructed a free-space panoramic fluorescence diffuse optical tomography system to take advantage of
co-registered microCT data acquired from the same animal. In this article, we present a data processing strategy that
adaptively selects the optical sampling points in the raw 2-D fluorescent CCD images. Specifically, the general sampling
area and sampling density are initially specified to create a set of potential sampling points sufficient to cover the region
of interest. Based on 3-D anatomical information from the microCT and the fluorescent CCD images, data points are
excluded from the set when they are located in an area where either the forward model is known to be problematic (e.g.,
large wrinkles on the skin) or where the signal is unreliable (e.g., saturated or low signal-to-noise ratio). Parallel Monte
Carlo software was implemented to compute the sensitivity function for image reconstruction. Animal experiments were
conducted on a mouse cadaver with an artificial fluorescent inclusion. Compared to our previous results using a finite
element method, the newly developed parallel Monte Carlo software and the adaptive sampling strategy produced
favorable reconstruction results.
We present a 3-D image reconstruction method for free-space fluorescence tomography of mice using hybrid anatomical prior information. Specifically, we use an optically reconstructed surface of the experimental animal and a digital mouse atlas to approximate the anatomy of the animal as structural priors to assist image reconstruction. Experiments are carried out on a cadaver of a nude mouse with a fluorescent inclusion (2.4-mm-diam cylinder) implanted in the chest cavity. Tomographic fluorescence images are reconstructed using an iterative algorithm based on a finite element method. Coregistration of the fluorescence reconstruction and micro-CT (computed tomography) data acquired afterward show good localization accuracy (localization error 1.2±0.6 mm). Using the optically reconstructed surface, but without the atlas anatomy, image reconstruction fails to show the fluorescent inclusion correctly. The method demonstrates the utility of anatomical priors in support of free-space fluorescence tomography.
KEYWORDS: Luminescence, Imaging systems, Monte Carlo methods, Reconstruction algorithms, Light sources, Sensors, CCD cameras, In vivo imaging, Image restoration, Photons
Fluorescence imaging is an important tool for tracking molecular-targeting probes in preclinical studies. It offers high
sensitivity, but nonetheless low spatial resolution compared to other leading imaging methods such CT and MRI. We
demonstrate our methodological development in small animal in vivo whole-body imaging using fluorescence
tomography. We have implemented a noncontact fluid-free fluorescence diffuse optical tomography system that uses a
raster-scanned continuous-wave diode laser as the light source and an intensified CCD camera as the photodetector. The
specimen is positioned on a motorized rotation stage. Laser scanning, data acquisition, and stage rotation are controlled
via LabVIEW applications. The forward problem in the heterogeneous medium is based on a normalized Born method,
and the sensitivity function is determined using a Monte Carlo method. The inverse problem (image reconstruction) is
performed using a regularized iterative algorithm, in which the cost function is defined as a weighted sum of the L-2
norms of the solution image, the residual error, and the image gradient. The relative weights are adjusted by two
independent regularization parameters. Our initial tests of this imaging system were performed with an imaging phantom
that consists of a translucent plastic cylinder filled with tissue-simulating liquid and two thin-wall glass tubes containing
indocyanine green. The reconstruction is compared to the output of a finite element method-based software package
NIRFAST and has produced promising results.
KEYWORDS: Reconstruction algorithms, Brain, Functional magnetic resonance imaging, Monte Carlo methods, Signal processing, Sensors, Absorption, Signal to noise ratio, Spectroscopy, Scattering
We introduce a new algorithm for the reconstruction of functional brain activations from near-infrared spectroscopic imaging (NIRSI) data. While NIRSI offers remarkable biochemical specificity, the attainable spatial resolution with this technique is rather limited, mainly due to the highly scattering nature of brain tissue and the low number of measurement channels. Our approach exploits the support-limited (spatially concentrated) nature of the activations to make the reconstruction problem well-posed. The new algorithm considers both the support and the function values of the activations as unknowns and estimates them from the data. The support of the activations is represented using a level-set scheme. We use a two-step alternating iterative scheme to solve for the activations. Since our approach uses the inherent nature of functional activations to make the problem well-posed, it provides reconstructions with better spatial resolution, fewer artifacts, and is more robust to noise than existing techniques. Numerical simulations and experimental data indicate a significant improvement in the quality (resolution and robustness to noise) over standard techniques such as truncated conjugate gradients (TCG) and simultaneous iterative reconstruction technique (SIRT) algorithms. Furthermore, results on experimental data obtained from simultaneous functional magnetic resonance imaging (fMRI) and optical measurements show much closer agreement of the optical reconstruction using the new approach with fMRI images than TCG and SIRT.
We use our new combined functional near infrared spectro-imaging (fNIRSI) and magnetic resonance imaging (MRI) technique to compare fMRI and fNIRSI data at different activation conditions, to obtain new information about the underlying physiology of the blood oxygen level dependent (BOLD) signal used in fMRI, and to assess statistical characteristics of spatial functional information provided by the group analysis of fNIRSI data. To achieve these goals we have acquired simultaneously fNIRSI and fMRJ data during the presentation of the checkerboard reversing with different frequencies, and analyzed these data following the standard correlation and group analysis of variance pathway used in functional neuroimaging. We have found that while the time courses of oxy-, deoxy-, and total- hemoglobin responses are equally well correlated with the time course of the BOLD response, the spatial pattern and magnitude of the BOLD response is better related to those of the oxy-, and total- hemoglobin responses rather than to the deoxyhemoglobin response. The statistical significance of the fNIRSI group maps is inferior to that of fMRI, and can be particularly compromised by the anatomical features of subjects.
We use near-infrared spectroscopy to investigate hemodynamic changes in humans during a breath holding exercise and their influence on the BOLD fMRI signal. We have quantitatively compared the BOLD fMRI signals with the hemoglobin concentration changes using correlation analysis of NIRS and fMRI data.
The integration of near-infrared (NIR) and functional MRI (fMRI) studies is potentially a powerful method to investigate the physiological mechanism of human cerebral activity. However, current NIR methodologies do not provide adequate accuracy of localization and are not fully integrated with MRI in the sense of mutual enhancement of the two imaging modalities. Results are presented to address these issues by developing an MRI-compatible optical probe and using diffuse optical tomography for optical image reconstruction. We have developed a complete methodology that seamlessly integrates NIR tomography with fMRI data acquisition. In this paper, we apply this methodology to determine both hemodynamic and early neuronal responses in the visual cortex in humans. Early results indicate that the changes in deoxyhemoglobin concentration from optical data are co-localized with fMRI BOLD signal changes, but changes in oxyhemoglobin concentration (not measurable using fMRI) show small spatial differences.
KEYWORDS: Magnetic resonance imaging, Photons, Monte Carlo methods, Head, Image restoration, Functional magnetic resonance imaging, Sensors, Optical properties, 3D image processing, Near infrared
We present an integrated methodology for human brain mapping by simultaneous BOLD fMRI and NIR imaging. This methodology consists of three innovative components: the construction of MRI-compatible optical probes that can be affixed to any part of the human head inside a standard MRI head-coil with minimal MR image distortion, the accurate determination of optode positions on the head from MR images, and the application of a perturbation approach and Monte Carlo method to compute the integral kernel of the Born solution to the diffusion equation for baseline optical properties. This integrated approach has been used to demonstrate promising capabilities for studying functional hemodynamic activation in human visual cortex by simultaneous fMRI and NIR tomography.
Near-infrared spectro-imaging (NIRSI) is a quickly developing method for the in-vivo imaging of biological tissues. In particular, it is now extensively employed for imaging the human brain. In this non-invasive technique, the information about the brain is obtained from the analysis of spatial light bundles formed by the photons traveling from light sources to detectors placed on the surface of the head. Most significant problems in the functional brain NIRSI are the separation of the brain information from the physiological noise in non-cerebral tissues, and the localization of functional signals. In this paper we describe signal and image processing techniques we developed in order to measure two types of functional cerebral signals: the hemodynamic responses, and neuronal responses.
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