Imaging workflow and calibration for CT-guided time-domain fluorescence tomography

Tichauer, Kenneth M.; Holt, Robert W.; El-Ghussein, Fadi; Zhu, Qingsong; Dehghani, Hamid; Leblond, Frédéric; Pogue, Brian W.

doi:10.1364/boe.2.003021

Cited by 24 publications

(32 citation statements)

References 43 publications

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“…In the presented methodology, the respective sensitivities of each detection channel are accounted for by collecting a baseline measurement of excitation light transmitted through a line-diffusor designed to direct equal fractions of light to each detector 15 . Furthermore, the detected light during an experiment is continuously calibrated to the laser reference, in terms of both intensity and mean-time, which could fluctuate over time, by the operation of a laser reference channel 11,15 . The second critical step is the accurate collection and co-registration of anatomical imaging for guided fluorescence reconstructions.…”

Section: Discussionmentioning

confidence: 99%

“…The FT data alone offers no anatomical information; therefore, in order to create a model of light transport that can be used to reconstruct the location of fluorescent sources within a specimen from the detected fluorescence at the surface of the specimen, the anatomy of the specimen in relation to the FT system must be accurately known. In our system, the anatomical information is acquired by a micro-computed tomography system with spatial coordinates that have been spatially registered with those of the FT system 15,20 . The third critical step involves ensuring that an optimal exposure (i.e., total photon detection time for each laser projection) is employed at every source-detector position.…”

Section: Discussionmentioning

confidence: 99%

“…The time-dimension of J is also convolved with the detector specific instrument response functions. x is a representation of the fluorescence map of interest and is solved for using a Levenberg-Marqardt non-negative least squares approach with Tikhonov regularization 15 .…”

Section: Discussionmentioning

confidence: 99%

“…The scaling factor is chosen to maximize EGFR binding contrast. Perform time-domain image reconstruction with the calibrated difference data using the TPSF for each detection channel as an input, and create fluorescence maps of the contrast-enhanced targeted tracer 15 .…”

Section: Image Reconstructionmentioning

confidence: 99%

“…This provides a simultaneous collection of transmitted excitation and emission light, and includes automatic fluorescence excitation exposure control 14 , laser referencing, and co-registration with a small animal computed tomography (microCT) system 15 . A nude mouse model was used for imaging.…”

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confidence: 99%

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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Tichauer

Holt

Samkoe

et al. 2012

JoVE

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Small animal fluorescence molecular imaging (FMI) can be a powerful tool for preclinical drug discovery and development studies 1 . However, light absorption by tissue chromophores (e.g., hemoglobin, water, lipids, melanin) typically limits optical signal propagation through thicknesses larger than a few millimeters 2 . Compared to other visible wavelengths, tissue absorption for red and near-infrared (near-IR) light absorption dramatically decreases and non-elastic scattering becomes the dominant light-tissue interaction mechanism. The relatively recent development of fluorescent agents that absorb and emit light in the near-IR range (600-1000 nm), has driven the development of imaging systems and light propagation models that can achieve whole body three-dimensional imaging in small animals 3 .Despite great strides in this area, the ill-posed nature of diffuse fluorescence tomography remains a significant problem for the stability, contrast recovery and spatial resolution of image reconstruction techniques and the optimal approach to FMI in small animals has yet to be agreed on. The majority of research groups have invested in charge-coupled device (CCD)-based systems that provide abundant tissue-sampling but suboptimal sensitivity [4][5][6][7][8][9] , while our group and a few others 10-13 have pursued systems based on very high sensitivity detectors, that at this time allow dense tissue sampling to be achieved only at the cost of low imaging throughput. Here we demonstrate the methodology for applying single-photon detection technology in a fluorescence tomography system to localize a cancerous brain lesion in a mouse model.The fluorescence tomography (FT) system employed single photon counting using photomultiplier tubes (PMT) and information-rich time-domain light detection in a non-contact conformation 11. This provides a simultaneous collection of transmitted excitation and emission light, and includes automatic fluorescence excitation exposure control 14 , laser referencing, and co-registration with a small animal computed tomography (microCT) system 15 . A nude mouse model was used for imaging. The animal was inoculated orthotopically with a human glioma cell line (U251) in the left cerebral hemisphere and imaged 2 weeks later. The tumor was made to fluoresce by injecting a fluorescent tracer, IRDye 800CW-EGF (LI-COR Biosciences, Lincoln, NE) targeted to epidermal growth factor receptor, a cell membrane protein known to be overexpressed in the U251 tumor line and many other cancers 18 . A second, untargeted fluorescent tracer, Alexa Fluor 647 (Life Technologies, Grand Island, NY) was also injected to account for non-receptor mediated effects on the uptake of the targeted tracers to provide a means of quantifying tracer binding and receptor availability/density 27 . A CT-guided, time-domain algorithm was used to reconstruct the location of both fluorescent tracers (i.e., the location of the tumor) in the mouse brain and their ability to localize the tumor was verified by contrast-enhanced magnet...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Image Reconstructionmentioning

confidence: 99%

mentioning

confidence: 99%

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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Tichauer

Holt

Samkoe

et al. 2012

JoVE

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Multi-modal molecular diffuse optical tomography system for small animal imaging

Guggenheim

Basevi

Frampton

et al. 2013

Meas. Sci. Technol.

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A multi-modal optical imaging system for quantitative 3D bioluminescence and functional diffuse imaging is presented, which has no moving parts and uses mirrors to provide multi-view tomographic data for image reconstruction. It is demonstrated that through the use of trans-illuminated spectral near infrared measurements and spectrally constrained tomographic reconstruction, recovered concentrations of absorbing agents can be used as prior knowledge for bioluminescence imaging within the visible spectrum. Additionally, the first use of a recently developed multi-view optical surface capture technique is shown and its application to model-based image reconstruction and free-space light modelling is demonstrated. The benefits of model-based tomographic image recovery as compared to 2D planar imaging are highlighted in a number of scenarios where the internal luminescence source is not visible or is confounding in 2D images. The results presented show that the luminescence tomographic imaging method produces 3D reconstructions of individual light sources within a mouse-sized solid phantom that are accurately localised to within 1.5mm for a range of target locations and depths indicating sensitivity and accurate imaging throughout the phantom volume. Additionally the total reconstructed luminescence source intensity is consistent to within 15% which is a dramatic improvement upon standard bioluminescence imaging. Finally, results from a heterogeneous phantom with an absorbing anomaly are presented demonstrating the use and benefits of a multi-view, spectrally constrained coupled imaging system that provides accurate 3D luminescence images.

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In vivovalidation of quantitative frequency domain fluorescence tomography

et al. 2012

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Imaging workflow and calibration for CT-guided time-domain fluorescence tomography

Cited by 24 publications

References 43 publications

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Multi-modal molecular diffuse optical tomography system for small animal imaging

In vivovalidation of quantitative frequency domain fluorescence tomography

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