Diffuse Luminescent Imaging Tomography
Diffuse Luminescent Imaging Tomography - Related Videos
12:24
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Cited by 18 •
2012
Diffuse fluorescence tomography offers a relatively low-cost and potentially high-throughout approach to preclinical in vivo tumor imaging. The methodology of optical data collection, calibration, and image reconstruction is presented for a computed tomography-guided non-contact time-domain system using fluorescent targeting of the tumor biomarker epidermal growth factor receptor in a mouse glioma model.
16:44
Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
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Cited by 15 •
2009
We suggest a Born normalized approach for Optical Projection Tomography (BnOPT) that accounts for the absorption properties of imaged samples to obtain accurate and quantitative fluorescence tomographic reconstructions. We use the proposed algorithm to reconstruct the fluorescence molecular probe distribution within small animal organs.
08:45
Cerenkov Luminescence Imaging (CLI) for Cancer Therapy Monitoring
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Cited by 8 •
2012
Use of Cerenkov Luminescence Imaging (CLI) for monitoring preclinical cancer treatment is described here. This method takes advantage of Cerenkov Radiation (CR) and optical imaging (OI) to visualize radiolabeled probes and thus provides an alternative to PET in preclinical therapeutic monitoring and drug screening.
11:51
Mesoscopic Fluorescence Tomography for In-vivo Imaging of Developing Drosophila
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Cited by 9 •
2009
Mesoscopic fluorescence tomography operates beyond the penetration limits of tissue-sectioning fluorescence microscopy. The technique is based on multi-projection illumination and a photon transport description. We demonstrate in-vivo whole-body 3D visualization of the morphogenesis of GFP-expressing wing imaginal discs in Drosophila melanogaster.
10:46
Diffusion Imaging in the Rat Cervical Spinal Cord
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Cited by 10 •
2015
The goal of this protocol is to obtain high-quality diffusion weighted magnetic resonance imaging (DWI) of the rat spinal cord for noninvasive characterization of tissue microstructure. This protocol describes optimizations of the MRI sequence, radiofrequency coil, and analysis methods to enable DWI images free from artifacts.
FAQs
The reconstruction treats measured surface light as evidence about hidden emitters. A tissue-diffusion model estimates how luminescence could travel through scattering tissue, and an inverse-problem solver works backward to infer the most plausible three-dimensional source distribution. This modeling step converts weak external measurements into estimates of where luminescent activity occurs and how intense it is.
Scattering changes the path and distribution of light before it reaches the tissue surface, so surface intensity does not directly identify the location of its source. Diffuse Luminescent Imaging Tomography accounts for this transport through a diffusion model. Without that correction, researchers could misinterpret the measured signal, particularly when estimating the depth or spatial distribution of luminescent activity.
These are separate outputs of the reconstruction. Source localization indicates where labeled cells or molecular probes are likely positioned in three-dimensional tissue, whereas intensity estimation indicates the strength of the underlying luminescent activity. Considering both dimensions helps cancer researchers examine spatial distribution together with changes in biological signal, such as altered gene expression or treatment response.
The method can analyze light emitted by labeled cells or molecular probes. In cancer research, those signals may represent tumor-cell distribution, gene expression, or a biological response to therapy. The choice of luminescent source determines what the reconstructed pattern can indicate, allowing the same imaging framework to support different questions about tumor biology in living model systems.
A study begins by observing luminescence at the tissue surface from labeled cells or molecular probes. Researchers then represent the signal's diffusion through the scattering tissue and solve the resulting inverse problem. The output is a three-dimensional estimate of source location and intensity, which can be examined to investigate cancer-related biology or changes during an experiment.
It is useful when investigators need noninvasive spatial information about luminescent tumor-related signals in living model systems. Applications include localizing tumor cells, following gene-expression signals, and monitoring therapeutic responses over time. Because the method does not use ionizing radiation, it can complement anatomical and molecular imaging approaches in preclinical studies rather than replacing them.