Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain

Optics Express (Impact Factor: 3.49). 03/2006; 14(3):1125-44. DOI: 10.1364/OE.14.001125
Source: PubMed


Diffuse optical correlation methods were adapted for three-dimensional (3D) tomography of cerebral blood flow (CBF) in small animal models. The image reconstruction was optimized using a noise model for diffuse correlation tomography which enabled better data selection and regularization. The tomographic approach was demonstrated with simulated data and during in-vivo cortical spreading depression (CSD) in rat brain. Three-dimensional images of CBF were obtained through intact skull in tissues(~4mm) deep below the cortex.

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Available from: Joel H Greenberg, Dec 17, 2013
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    • "Studies have shown that infarct volume highly depends on reduction of CBF during cerebral ischemia and perfusion recovery from ischemia (Soriano et al., 1997). There are several techniques providing rCBF measurements, including single photon emission tomography (SPECT) (Kwiatek et al., 2000; Pavics et al., 1999), CT, positron emission tomography (PET) (Johnson et al., 1999; Tuominen et al., 2004), perfusion-weighted MRI, diffuse correlation spectroscopy (DCS) (Shang et al., 2011; Zhou et al., 2006), and laser Doppler flowmetry (Liu et al., 2008; Tonnesen et al., 2005). All of these techniques have been adopted in human and animal studies. "
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    ABSTRACT: Diffuse optical tomography (DOT) has been used by several groups to assess cerebral hemodynamics of cerebral ischemia in humans and animals. In this study, we combined DOT with an indocyanine green (ICG)-tracking method to achieve interleaved images of cerebral hemodynamics and blood flow index (BFI) using two middle cerebral artery occlusion (MCAO) rat models. To achieve volumetric images with high-spatial resolution, we first integrated a depth compensation algorithm (DCA) with a volumetric mesh-based rat head model to generate three-dimensional (3D) DOT on a rat brain atlas. Then, the experimental DOT data from two rat models were collected using interleaved strategy for cerebral hemodynamics and BFI during and after ischemic stroke, with and without a thrombolytic therapy for the embolic MCAO model. The acquired animal data were further analyzed using the integrated rat-atlas-guided DOT method to form time-evolving 3D images of both cerebral hemodynamics and BFI. In particular, we were able to show and identify therapeutic outcomes of a thrombolytic treatment applied to the embolism-induced ischemic model. This paper demonstrates that volumetric DOT is capable of providing high-quality, interleaved images of cerebral hemodynamics and blood perfusion in small animals during and after ischemic stroke, with excellent 3D visualization and quantifications.
    NeuroImage 07/2013; 85. DOI:10.1016/j.neuroimage.2013.07.020 · 6.36 Impact Factor
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    • ") can be used to estimate the noise level of DCS mea- surements. For the case of diffuse reflectance measurement on medium surface, a semi-infinite geometry should be considered instead and (3) should be used to calculate [g 2 (τ ) – 1] rather than the assumption of exponential decay function as used in [28]. However , mathematically, it is difficult to derive a noise model due to the complexity of (3). "
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    ABSTRACT: Near-infrared diffuse correlation spectroscopy (DCS) has recently been employed for noninvasive acquisition of blood flow information in deep tissues. Based on the established correlation diffusion equation, the light intensity autocorrelation function detected by DCS is determined by a blood flow index áDB, tissue absorption coefficient ìa, reduced scattering coefficient ìs, and a coherence factor â. The present study is designed to investigate the possibility of extracting multiple parameters such as ìa, ìs, â, and áDB through fitting one single autocorrelation function curve and evaluate the performance of different fitting methods. For this purpose, computer simulations, tissue-like phantom experiments and in-vivo tissue measurements were utilized. The results suggest that it is impractical to simultaneously fit áDB and ìa or áDB and ìs from one single autocorrelation function curve due to the large crosstalk between these paired parameters. However, simultaneously fitting â and áDB is feasible and generates more accurate estimation with smaller standard deviation compared to the conventional two-step fitting method (i.e., first calculating â and then fitting áDB). The outcomes from this study provide a crucial guidance for DCS data analysis.
    IEEE transactions on bio-medical engineering 11/2012; 60(2). DOI:10.1109/TBME.2012.2226885 · 2.35 Impact Factor
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    • "Here < ∆r 2 (r, τ) > represents the mean square displacement (MSD) suffered by the particle at r. We assume that MSD has a linear time evolution given by < ∆r 2 (r, τ) >= 6D B (r)τ, where D B (r) is the time independent particle diffusion coefficient related to the viscosity η of the medium [15]. We use the mixed boundary condition, to solve the propagation equation (2.2) for G(r, τ): "
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    ABSTRACT: The aim of this article is to study the mathematical analysis for an inverse problem and its numerical implementation associated with diffuse correlation tomography. The coefficients of the diffusion equation governing the propagation of field autocorrelation through a turbid medium (tissue-like) depend on both the optical and mechanical properties of the medium. Assuming the mechanical property is given by a time independent particle diffusion coefficient (D B ), we consider the development of regularized Gauss-Newton algorithm for the recovery of D B from boundary measurements. We study the existence and uniqueness of the forward problem and also for the Fréchet derivative operator which are essential for convergence study. The nonlinear minimization problem associated with the recovery of D B is locally linearized and solved through a regularized Gauss-Newton algorithm. The conditions to be satisfied for the convergence of the Gauss-Newton algorithm are established. Finally, the method is proven through numerical recovery of D B from intensity autocorrelation measured at the boundary. Once D B is obtained one can also recover other mechanical properties.
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