1Thayer School of Engineering, Dartmouth College, 2Department of Physics and Astronomy, Dartmouth College, 3Darmouth Medical School, Dartmouth College, 4School of Computer Science, University of Birmingham
Tichauer, K. M., Holt, R. W., Samkoe, K. S., El-Ghussein, F., Gunn, J. R., Jermyn, M., et al. Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers. J. Vis. Exp. (65), e4050, doi:10.3791/4050 (2012).
Small animal fluorescence molecular imaging (FMI) can be a powerful tool for preclinical drug discovery and development studies1. However, light absorption by tissue chromophores (e.g., hemoglobin, water, lipids, melanin) typically limits optical signal propagation through thicknesses larger than a few millimeters2. 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 animals3.
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 sensitivity4-9, while our group and a few others10-13 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 conformation11. This provides a simultaneous collection of transmitted excitation and emission light, and includes automatic fluorescence excitation exposure control14, laser referencing, and co-registration with a small animal computed tomography (microCT) system15. 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 cancers18. 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/density27. 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 magnetic resonance imaging.
Though demonstrated for fluorescence imaging in a glioma mouse model, the methodology presented in this video can be extended to different tumor models in various small animal models potentially up to the size of a rat17.
1. Animal Preparation
2. Fluorescence Tomography System Calibration
3. Imaging Protocol
4. Image Reconstruction
5. Representative Results
An example of a fluorescence reconstruction overlaid with a co-registered CT anatomical image from the head of a mouse with a U251 orthotopic glioma tumor is presented in Figure 1b. The center of mass of the glioma determined by the fluorescent reconstruction (Figure 1b) was within 1 mm of the tumor center of mass determined by contrast-enhance magnetic resonance imaging (Figure 1a). The CT and MRI images were co-registered based on a mutual-information transformation.
Figure 1. Contrast-enhanced (Gadolinium) magnetic resonance image of mouse head (a). The mouse was inoculated orthotopically with a U251 human glioma cell line. The location of the tumor, which absorbs more contrast agent than the normal brain, can be seen in the left cerebral hemisphere (right in image) and indicated by the white arrow. The corresponding computed tomography image (from the same location on the mouse head) is depicted in (b) with the epidermal growth factor targeted fluorescence minus the untargeted fluorescence reconstruction overlaid. The units of fluorescence are in inverse mm and relate to the absorption coefficient of bound targeted fluorescence multiplied by its quantum efficiency and by its concentration.
Fluorescence tomography (FT) is a sensitive, ionizing radiation free molecular imaging modality based on visible and near-infrared light transport through biological tissue. Most of the interest in FT has been focused on its potential to expedite drug discovery and development in small animal experimental models1 and one key area of research has been the study of cancer biomarker expression and response to molecular therapies26. At present, there are two competing approaches to FT system design. The most common design is based on cooled charge-coupled device (CCD) cameras for fluorescence detection4-9. This design provides a high density of measurements, maximizing tissue sampling since each pixel in the CCD camera can detect light that has traveled a unique path through the tissue. However, CCD cameras have a limited dynamic range and read-out noise limits their ultimate sensitivity. The second design avoids the potential limitations of CCD camera detection by employing highly sensitive single-photon counting technology based on the use of such detectors as photomultiplier tubes or avalanche photodiodes10-13. The drawback of these more sensitive detection methods is that each detector can only collect light at a single point; therefore, to achieve dense tissue sampling, either many detectors have to be used (which is very expensive), or many projections have to be imaged with the same detector (which can be time consuming). While the optimal level of tissue sampling for small animal FT has not been agreed upon, and may vary on a case-by-case basis, it is agreed that single-photon counting instrumentation is better suited to explore the sensitivity limits of FT in terms of its ability to detect low concentrations of molecular markers. In this study, we provide a methodology for carrying out FT using single-photon counting detection instrumentation to localize tumors in mice.
There are four critical steps involved to produce robust datasets with time-correlated single-photon counting FT. The first is the application of a suitable and straightforward calibration procedure. 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 detector15. 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 channel11,15. The second critical step is the accurate collection and co-registration of anatomical imaging for guided fluorescence reconstructions. 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 system15,20. 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. This is important for two reasons: first, to ensure that there is adequate signal-to-noise at each detection position and second to avoid detector saturation, which could damage the detection units. In order to achieve optimal exposure at each detector position, an automatic exposure control is employed, which essentially triangulates the optimum exposure from two, low-signal exposures14. The fourth critical step of the methodology is referencing the collected fluorescence data to the amount of transmitted excitation light. This referencing is often called the Born ratio, and provides many benefits for FT, with the main one being a mitigation of model-data mismatch errors23,24. The presented system was designed to detect both fluorescence and transmitted excitation light simultaneously by channeling the light in each detection channel into 2 separate photomultiplier tubes. By doing this, we avoid any effects of motion on the accuracy of the Born ratio.
With a robust dataset it hand, image reconstruction of time-domain data involves solving the inverse problem of the finite element mesh having the expression:
where d is a vector with n x m elements for n source-detector projections and m TPSF time gates; J is an n x m-by-l sensitivity matrix (or Jacobian), for l nodes in the mesh; and x is the vector of fluorescence optical properties in each node, having size l. d is the calibrated data collected during the experiment and J is simulated using the finite element solution to the time domain diffusion approximation of fluorescence transport25. 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 regularization15.
The methodology presented here, which describes a procedure capable of localizing fluorescently labeled tumors in mice using highly-sensitive photon counting fluorescence detection, has the potential to push the limits of FT. In a previous study, the potential of employing this approach in larger-than-mice animals models, such as rats, as well as improved sensitivity over existing system designs in mouse-sized specimens, was demonstrated17. The immediate application of this approach would be for the monitoring of biomarker expression in vivo in small animal tumor models to assess drug efficacy in a high-throughput means. The ability of the system to excite and detect fluorescence at multiple wavelengths allows the simultaneous detection of multiple fluorescent markers. Additional fluorescent markers provide a means of interrogating multiple aspects of a pathology, simultaneously, or could be used, as in this study, to employ more quantitative imaging approaches such as dual-reporter methods of measuring in vivo binding potential, a marker of receptor density26,27.
No conflicts of interest declared.
This work has been funded by National Cancer Institute's grants R01 CA120368, R01 CA109558 (KMT, RWH, FEG, BWP), RO1 CA132750 (MJ, BWP) and K25 CA138578 (FL), and Canadian Institutes of Health Research postdoctoral fellowship award (KMT). The development of the fluorescence tomography system was partially funded by Advanced Research Technologies (Montreal, QC).
|IRDye 800CW-EGF||LI-COR Biosciences||926-08446|
|Alexa Fluor 647, succinimidyl ester||Life Technologies||A20106||Reacted with water to minimize non-specific binding|