Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging

We outline a computationally efficient image correction algorithm, which we have applied to diffuse optical tomography (DOT) image time series derived from a magnetic resonance imaging (MRI)-based brain model. Results show that the algorithm increases spatial resolution, decreases spatial bias, and only modestly reduces temporal accuracy for noise levels typically seen in experiment, and produces results comparable to image reconstructions that incorporate information from MRI priors. We demonstrate that this algorithm has robust performance in the presence of noise, background heterogeneity, irregular external and internal boundaries, and error in the initial guess. However, the algorithm introduces artifacts when the absorption and scattering coefficients of the reference medium are overestimated--a situation that is easily avoided in practice. The considered algorithm offers a practical approach to improving the quality of images from time-series DOT, even without the use of MRI priors.     

Coregistration of diffuse optical spectroscopy and magnetic resonance imaging in a rat tumor model

We report coregistration of near-infrared diffuse optical spectroscopy (DOS) and magnetic resonance imaging (MRI) for the study of animal model tumors. A combined broadband steady-state and frequency-domain apparatus was used to determine tissue oxyhemoglobin, deoxyhemoglobin, and water concentration locally in tumors. Simultaneous MRI coregistration provided structural (T2-weighted) and contrast-enhanced images of the tumor that were correlated with the optical measurements. By use of Monte Carlo simulations, the optically sampled volume was superimposed on the MR images, showing precisely which tissue structure was probed optically. DOS and MRI coregistration measurements were performed on seven rats over 20 days and were separated into three tumor tissue classifications: viable, edematous, and necrotic. A ratio of water concentration to total hemoglobin concentration, as measured optically, was performed for each tissue type and showed values for edematous tissue to be greater than viable tissue (1.2 +/- 0.49 M/microM versus 0.48 +/- 0.15 M/microM). Tissue hemoglobin oxygen saturation (StO2) also showed a large variation between tissue types: viable tissue had an optically measured StO2 value of 61 +/- 5%, whereas StO2 determined for necrotic tissue was 43 +/- 6%.     

Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging

Although most current diffuse optical brain imaging systems use only nearest- neighbor measurement geometry, the spatial resolution and quantitative accuracy of the imaging can be improved through the collection of overlapping sets of measurements. A continuous-wave diffuse optical imaging system that combines frequency encoding with time-division multiplexing to facilitate overlapping measurements of brain activation is described. Phantom measurements to confirm the expected improvement in spatial resolution and quantitative accuracy are presented. Experimental results showing the application of this instrument for imaging human brain activation are also presented. The observed improvement in spatial resolution is confirmed by functional magnetic resonance imaging.     

Noncontact fluorescence diffuse optical tomography of heterogeneous media

Fluorescence-enhanced diffuse optical tomography is expected to be useful to the collection of functional information from small animal models. This technique is currently limited by the extent of tissue heterogeneity and management of the shape of the animals. We propose an approach based on the reconstruction of object heterogeneity, which provides an original solution to the two problems. Three evaluation campaigns are described: the first two were performed on phantoms designed to test the reconstructions in highly heterogeneous media and noncontact geometries; the third was conducted on mice with lung tumors to test fluorescence yield reconstruction feasibility in vivo.     

Self-similar correlation function in brain resting-state functional magnetic resonance imaging

Adaptive behaviour, cognition and emotion are the result of a bewildering variety of brain spatio-temporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain''s 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e. they are functionally integrated. Here, we investigate these large-scale dynamical properties by inspecting the character of the spatio-temporal correlations of brain resting-state activity. In physical systems, these correlations in space and time are captured by measuring the correlation coefficient between a signal recorded at two different points in space at two different times. We show that this two-point correlation function extracted from resting-state functional magnetic resonance imaging data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/f frequency behaviour of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. This dynamical property may be seen as an important marker of brain well-being in both health and disease.     

Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography

In this study, we first propose the use of edge-preserving regularization in optimizing an ill-conditioned problem in the reconstruction procedure for diffuse optical tomography to prevent unwanted edge smoothing, which usually degrades the attributes of images for distinguishing tumors from background tissues when using Tikhonov regularization. In the edge-preserving regularization method presented here, a potential function with edge-preserving properties is introduced as a regularized term in an objective function. With the minimization of this proposed objective function, an iterative method to solve this optimization problem is presented in which half-quadratic regularization is introduced to simplify the minimization task. Both numerical and experimental data are employed to justify the proposed technique. The reconstruction results indicate that edge-preserving regularization provides a superior performance over Tikhonov regularization.     

Volumetry and topography of the human brain by magnetic resonance imaging

Software, termed Morph©, is described for the morphometric analysis of magnetic resonance images of the human brain. Algorithms for objective contrast border recognition, surface feature classification, and surface feature contour unfolding are evaluated. Intraoperator and interoperator variabilities and errors were determined to be less than 2% over a group of operators (n = 6) for the known volume of a cerebral hemisphere obtained at autopsy. Volumetric errors were measured to be ± 3% for simulated objects and less than 1% for images of phantoms. Contours of brains of normal elderly subjects (n = 6) and patients with probable Alzheimer's disease (n = 6), segmented into sulcal and gyral features to determine gyrification indices, showed concordance with literature values. Flat maps or topograms were obtained of the convoluted cortex by unfolding the segmented contours. The areas of surface features were readily obtained. The activation of the frontal eye fields (FEF) defined by functional magnetic resonance imaging (fMRI) with an oculomotor control task was mapped onto a topogram of the precentral sulcus. This software provides accurate volumetric analysis with additional topographical tools for characterizing convoluted cortical features and for presenting three‐dimensional fMRI activation patterns as two‐dimensional maps. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 198–208, 2000     

In vivo quantification of optical contrast agent dynamics in rat tumors by use of diffuse optical spectroscopy with magnetic resonance imaging coregistration

We present a study of the dynamics of optical contrast agents indocyanine green (ICG) and methylene blue (MB) in an adenocarcinoma rat tumor model. Measurements are conducted with a combined frequency-domain and steady-state optical technique that facilitates rapid measurement of tissue absorption in the 650-1000-nm spectral region. Tumors were also imaged by use of contrast-enhanced magnetic resonance imaging (MRI) and coregistered with the location of the optical probe. The absolute concentrations of contrast agent, oxyhemoglobin, deoxyhemoglobin, and water are measured simultaneously each second for approximately 10 min. The differing tissue uptake kinetics of ICG and MB in these late-stage tumors arise from differences in their effective molecular weights. ICG, because of its binding to plasma proteins, behaves as a macromolecular contrast agent with a low vascular permeability. A compartmental model describing ICG dynamics is used to quantify physiologic parameters related to capillary permeability. In contrast, MB behaves as a small-molecular-weight contrast agent that leaks rapidly from the vasculature into the extravascular, extracellular space, and is sensitive to blood flow and the arterial input function.     

Fetal brain extraction from magnetic resonance image (MRI) of human fetus

Study of a fetus is a rapidly growing field of research and it requires fetal brain segmentation. Automatic segmentation of the fetal brain from magnetic resonance imaging (MRI) is challenging, due to the highly variable size and shape of the developing brain, possible brain structure abnormalities, movement of the fetus and a poor resolution of fetal MRI scans. This is in contrast to adult brain segmentation, where the brain structure is stable and several established methods exist. This paper presents a fully automatic segmentation method to segment the fetal brain portion from MRI. The segmentation pipeline developed in this study includes contrast enhancement, region growing and hole filling. Twenty-five volumes of retrospective fetal MRI are used in this work. Experimental results show that this method can successfully segment the fetal brain from magnetic resonance images which are comparable to that of a semi-automatic method.     

A reconstruction method for diffuse optical tomography by grouping of source-detector configuration

Image reconstruction for diffusion optical tomography often involves repeatedly solving the forward problem, wherein the diffusion equation is typically utilized to describe the light propagation. However, the diffusion equation may not be valid in regions of either high absorption or low scatter. In this paper, a new method is proposed to improve the forward model. This method is based on the modelling error between the Monte Carlo method and the diffusion equation. To further improve the efficiency of image reconstruction, we propose the grouping strategy of source-detector configuration. Simulation results demonstrate that the proposed method can significantly speed up the reconstruction process with the high reconstruction quality.     

Targeted nanoparticles for the non-invasive detection of traumatic brain injury by optical imaging and fluorine magnetic resonance imaging

Necrosis is a form of cell death that occurs only under pathological conditions such as ischemic diseases and traumatic brain injury (TBI). Non-invasive imaging of the affected tissue is a key component of novel therapeutic interventions and measurement of treatment responses in patients. Here, we report a bimodal approach for the detection and monitoring of TBI. PEGylated poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), encapsulating both near infrared (NIR) fluorophores and perfluorocarbons (PFCs), were targeted to necrotic cells. We used cyanine dyes such as IRDye 800CW, for which we have previously demonstrated specific targeting to intracellular proteins of cells that have lost membrane integrity. Here, we show specific in vivo detection of necrosis by optical imaging and fluorine magnetic resonance imaging (¹⁹F MRI) using newly designed PLGA NP(NIR700 + PFC)-PEG-800CW. Quantitative ex vivo optical imaging and ¹⁹F MR spectroscopy of NIR-PFC content in injured brain regions and in major organs were well correlated. Both modalities allowed the in vivo identification of necrotic brain lesions in a mouse model of TBI, with optical imaging being more sensitive than ¹⁹F MRI. Our results confirm increased blood pool residence time of PLGA NPs coated with a PEG layer and the successful targeting of TBI-damaged tissue. A single PLGA NP containing NIR-PFC enables both rapid qualitative optical monitoring of the TBI state and quantitative 3D information from deeper tissues on the extent of the lesion by MRI. These necrosis-targeting PLGA NPs can potentially be used for clinical diagnosis of brain injuries.

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Improvement of image quality in diffuse optical tomography by use of full time-resolved data

In the field of diffuse optical tomography (DOT), it is widely accepted that time-resolved (TR) measurement can provide the richest information on photon migration in a turbid medium, such as biological tissue. However, the currently available image reconstruction algorithms for TR DOT are based mostly on the cw component or some featured data types of original temporal profiles, which are related to the solution of a time-independent diffusion equation. Although this methodology can greatly simplify the reconstruction process, it suffers from low spatial resolution and poor quantitativeness owing to the limitation of effectively applicable data types. To improve image quality, it has been argued that exploiting the full TR data is essential. We propose implementation of a DOT algorithm by using full TR data and furthermore a variant algorithm with time slices of TR data to alleviate the computational complexity and enhance noise robustness. Compared with those algorithms where the featured data types are used, our evaluations on the spatial resolution and quantitativeness show that a significant improvement in imaging quality can be achieved when full TR data are used, which convinces the DOT community of the potential advantage of the TR domain over cw and frequency domains.     

Robust inference of baseline optical properties of the human head with three-dimensional segmentation from magnetic resonance imaging

We model the capability of a small (6-optode) time-resolved diffuse optical tomography (DOT) system to infer baseline absorption and reduced scattering coefficients of the tissues of the human head (scalp, skull, and brain). Our heterogeneous three-dimensional diffusion forward model uses tissue geometry from segmented magnetic resonance (MR) data. Handling the inverse problem by use of Bayesian inference and introducing a realistic noise model, we predict coefficient error bars in terms of detected photon number and assumed model error. We demonstrate the large improvement that a MR-segmented model can provide: 2-10% error in brain coefficients (for 2 x 10(6) photons, 5% model error). We sample from the exact posterior and show robustness to numerical model error. This opens up the possibility of simultaneous DOT and MR for quantitative cortically constrained functional neuroimaging.     

Fast multispectral diffuse optical tomography system for in vivo three-dimensional imaging of seizure dynamics

We describe a multispectral continuous-wave diffuse optical tomography (DOT) system that can be used for in vivo three-dimensional (3-D) imaging of seizure dynamics. Fast 3-D data acquisition is realized through a time multiplexing approach based on a parallel lighting configuration--our system can achieve 0.12 ms per source per wavelength and up to a 14 Hz sampling rate for a full set of data for 3-D DOT image reconstruction. The system is validated using both static and dynamic tissue-like phantoms. An initial in vivo experiment using a rat model of seizure is also demonstrated.     

T-matrix-based inverse algorithm for morphologic characterization of nonspherical particles using multispectral diffuse optical tomography

An inverse algorithm is presented for tomographically imaging morphologic characteristics of nonspherical particles in heterogeneous turbid media. The particles are assumed to have spheroidal shapes with random orientations. The inverse algorithm is based on a relationship of the particle scattering spectra, obtained from multispectral diffuse optical tomography, and the size, concentration, and aspect ratio of spheroidal particles through the T-matrix method. The algorithm is implemented based on Tikhonov-Marquardt regularization techniques that minimize the difference between the observed and calculated scattering spectra. Different statistical models are assumed for the suspended nonspherical particles and the performance of the inverse algorithm is tested using noise-corrupted data up to 50% noise added to the observed scattering spectra.     

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

In this article, we propose a new edge detecting method based on the transform coefficients obtained by a point spread function constructed out of Chebyshev's orthogonal polynomials. This edge detector finds edges similar to that of Prewitt and Roberts but is robust against additive and multiplicative noises. We also propose a new scheme to extract brain portion from the magnetic resonance images (MRI) of human head scan by making use of the of the new edge detector. The proposed scheme involves edge detection, morphological operations, and largest connected component analysis. Experiments conducted by applying the proposed scheme on 19 volumes of MRI collected from Internet Brain Segmentation Repository (IBSR) show that the proposed brain extraction scheme performed better than the popular Brain Extraction Tool (BET). The performance of the proposed scheme is measured by computing the Dice coefficient (D) and Jaccard similarity index (J). The proposed method produced a value of 0.9068 for D and 0.8321 for J.     

Significance of tissue anisotropy in optical tomography of the infant brain

We study the effect of tissue anisotropy in optical tomography of neonates. A Monte Carlo method capable of modeling photon migration in an arbitrary 3D tissue model with spatially varying optical properties and tissue anisotropy is used for simulating measurements of neonates. Anatomical and diffusion tensor magnetic resonance imaging of neonates are used for creating the anatomical models. We find that tissue anisotropy affects the measured signal and the pattern of sensitivity in optical measurements.     

Pseudodynamic systems approach based on a quadratic approximation of update equations for diffuse optical tomography

We explore a pseudodynamic form of the quadratic parameter update equation for diffuse optical tomographic reconstruction from noisy data. A few explicit and implicit strategies for obtaining the parameter updates via a semianalytical integration of the pseudodynamic equations are proposed. Despite the ill-posedness of the inverse problem associated with diffuse optical tomography, adoption of the quadratic update scheme combined with the pseudotime integration appears not only to yield higher convergence, but also a muted sensitivity to the regularization parameters, which include the pseudotime step size for integration. These observations are validated through reconstructions with both numerically generated and experimentally acquired data.     

Fuzzy clustering-based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Medical images are obtained with computer-aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy-clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real-world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama-Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C-means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel-based FCM σ, the output of which is also very good after EFCM.