A novel approach is presented for obtaining fast robust three-dimensional (3-D) reconstructions of bioluminescent reporters buried deep inside animal subjects from multispectral images of surface bioluminescent photon densities. The proposed method iteratively acts upon the equations relating the multispectral data to the luminescent distribution with high computational efficiency to provide robust 3-D reconstructions. Unlike existing algebraic reconstruction techniques, the proposed method is designed to use adaptive projections that iteratively guide the updates to the solution with improved speed and robustness. Contrary to least-squares reconstruction methods, the proposed technique does not require parameter selection or optimization for optimal performance. Additionally, optimized schemes for thresholding, sampling, and ordering of the bioluminescence tomographic data used by the proposed method are presented. The performance of the proposed approach in reconstructing the shape, volume, flux, and depth of luminescent inclusions is evaluated in a multitude of phantom-based and dual-modality in vivo studies in which calibrated sources are implanted in animal subjects and imaged in a dual-modality optical/computed tomography platform. Statistical analysis of the errors in the depth and flux of the reconstructed inclusions and the convergence time of the proposed method is used to demonstrate its unbiased performance, low error variance, and computational efficiency.
Spectral unmixing is a useful technique in fluorescence imaging for reducing the effects of native tissue autofluorescence and separating multiple fluorescence probes. While spectral unmixing methods are well established in fluorescence microscopy, they typically rely on precharacterized in-vitro spectra for each fluorophore. However, there are unique challenges for in-vivo applications, since the tissue absorption and scattering can have a significant impact on the measured spectrum of the fluorophore, and therefore make the in-vivo spectra substantially different to that of in vitro. In this work, we introduce a spectral unmixing algorithm tailored for in-vivo optical imaging that does not rely on precharacterized spectral libraries. It is derived from a multivariate curve resolution (MCR) method, which has been widely used in studies of chemometrics and gene expression. Given multispectral images and a few straightforward constraints such as non-negativity, the algorithm automatically finds the signal distribution and the pure spectrum of each component. Signal distribution maps help separate autofluorescence from other probes in the raw images and hence provide better quantification and localization for each probe. The algorithm is demonstrated with an extensive set of in-vivo experiments using near-infrared dyes and quantum dots in both epi-illumination and transillumination geometries.
KEYWORDS: Natural surfaces, Tomography, In vivo imaging, Tissue optics, Bandpass filters, Tissues, 3D image processing, Imaging systems, Image filtering, Chemical elements
A new method is described for obtaining a 3-D reconstruction of a bioluminescent light source distribution inside a living animal subject, from multispectral images of the surface light emission acquired on charge-coupled device (CCD) camera. The method uses the 3-D surface topography of the animal, which is obtained from a structured light illumination technique. The forward model of photon transport is based on the diffusion approximation in homogeneous tissue with a local planar boundary approximation for each mesh element, allowing rapid calculation of the forward Green's function kernel. Absorption and scattering properties of tissue are measured a priori as input to the algorithm. By using multispectral images, 3-D reconstructions of luminescent sources can be derived from images acquired from only a single view. As a demonstration, the reconstruction technique is applied to determine the location and brightness of a source embedded in a homogeneous phantom subject in the shape of a mouse. The technique is then evaluated with real mouse models in which calibrated sources are implanted at known locations within living tissue. Finally, reconstructions are demonstrated in a PC3M-luc (prostate tumor line) metastatic tumor model in nude mice.
Diffuse near-infrared tomography of tissue can provide intrinsically useful information about total hemoglobin, oxygen saturation, water and cytochromes within tissue, yet to extract this information spectral data is required at many wavelengths. In this study, we examine a new approach to using multispectral continuous wave measurements through tissue along with second-derivative data analysis methods to estimate the pathlength in tissues over multiple tomographic paths. The goal of this work has been to demonstrate that the optical differential pathlength that is estimated by spectroscopy methods is directly related to the optical pathlength as measured by frequency-domain signals. This direct relation then allows the use of tomographic algorithms which have been developed for frequency-domain optical tomography to be applied to multispectral continuous wave data. The theoretical development is presented here, along with numerical validation in homogeneous and heterogeneous tissue-simulating regions. These results indicate that the approach outlined is valid and provides the theoretical basis for developing multispectral near-infrared tomography of tissues.
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