All-optical anatomical co-registration formolecular imaging of small animals usingdynamic contrast

ELIZABETH M. C. HILLMAN1* AND ANNA MOORE2

1Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, 1210 Amsterdam Avenue, New York,

New York 10027, USA2Molecular Imaging Laboratory, MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Building 149,

13th Street, Charlestown, Massachusetts 02129, USA

*e-mail: eh2245@columbia.edu

Published online: 19 August 2007; doi:10.1038/nphoton.2007.146

Optical molecular imaging in small animals harnesses the powerof highly specific and biocompatible contrast agents for drugdevelopment and disease research1–7. However, the widespreadadoption of in vivo optical imaging has been inhibited by itsinability to clearly resolve and identify targeted internalorgans. Optical tomography8–11 and combined X-ray andmicro-computed tomography (micro-CT)12 approachesdeveloped to address this problem are generally expensive,complex or incapable of true anatomical co-registration. Here,we present a remarkably simple all-optical method that cangenerate co-registered anatomical maps of a mouse’s internalorgans, while also acquiring in vivo molecular imaging data.The technique uses a time series of images acquired afterinjection of an inert dye. Differences in the dye’s in vivobiodistribution dynamics allow precise delineation andidentification of major organs. Such co-registered anatomicalmaps permit longitudinal organ identification irrespective ofrepositioning or weight gain, thereby promising greatlyimproved accuracy and versatility for studies of orthotopicdisease, diagnostics and therapies.

Accurate optical molecular imaging of the internal organs ofsmall animals could revolutionize in vivo drug and diseaseresearch1–7. Yet, for non-invasive in vivo optical imaging, theeffects of intrinsic absorption and scattering distort andattenuate signals from any target deeper than a few millimetres.This makes the study of in situ orthotopic tumours or diseasedorgans highly challenging. Further, because targeted fluorescent9

and bioluminescent13 molecular probes are designed to labelonly targeted cells, typical images do not reveal adjacent orlandmark organs that could aid in identification of a targetedorgan. Autofluorescence and nonspecific labelling furtherconfound interpretation14. Superficial subcutaneous xenograftsare much simpler to measure, but often do not resemble thehuman disease; for example, transplanted cancer-cell xenografttumours are often surrounded by a pseudocapsule, have limitedchances to invade major anatomical structures and rarelyspread metastasis15,16.

These difficulties have motivated the development of complextomographic approaches and multimodality imaging systems thatare generally expensive, complex and often inaccurate. We sought

to devise a way to improve the interpretation of in vivo opticalimages of targeted contrast agents by providing an exactly co-registered anatomical overlay of the major internal organs of thesmall animal. In addition to being insensitive to animalrepositioning and weight gain, our approach is far simpler andmore cost-effective than existing approaches to anatomical co-registration. For example, three-dimensional optical tomographycan help improve estimates of an organ’s location, and improvequantitation and orientation (Xenogen IVIS 3D Series,refs 8–11). However, three-dimensional optical imaging is highlycomplex and expensive, and it still suffers from a lack oflandmark structures to allow the actual anatomical location ofthe probe to be identified. Multimodality optical systems includingX-ray (Kodak Image Station In-Vivo FX), micro-computedtomography (CT) (ref. 12), magnetic resonance imaging17 andultrasound significantly increase the cost and complexity of theimaging process. In addition, X-ray contrast is dominated bybone, and micro-CT and ultrasound contrast cannot delineateor identify all internal soft-tissue organs. Imaging geometrydifferences also make co-registration challenging. Multispectraloptical imaging can enhance the contrast of the target dye inthe presence of autofluorescence14, but has not been used todelineate organs.

In response to this evident need for a more accessible methodfor generating co-registered anatomical maps, we present asimple all-optical approach that exploits the in vivo dynamicsof an inert tracer dye as it circulates and accumulates indifferent organs following intravenous injection. We call thismethod dynamic fluorescence molecular imaging (DFMI).Each organ in the body plays a different role in circulating,accumulating or metabolizing a dye, and so each organ willexhibit a distinctive time course in its optical signal. Thisphenomenon has been documented in previous opticalimaging studies11,18,19, but has never been harnessed to directlyprovide anatomical information. Our approach also hasfoundations in perfusion imaging, an established techniqueused in positron-emission tomography, magnetic resonanceimaging20,21 and X-ray-CT (ref. 22) imaging, which exploitsthe dynamics of an injected dye to delineate differentfunctional structures.

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In this letter, we describe a dynamic optical imaging systemcapable of capturing the in vivo biodistribution kinetics of twodyes in parallel. We demonstrate two processing methods bywhich the characteristic temporal signatures of each organ can beexploited to allow spatial delineation of the major internalorgans. We also discuss how these dynamic imaging techniquescould be further used to evaluate organ function, and alsoimprove quantitative estimates of the targeted probe concentration.

DFMI data were acquired on five normal anaesthetized nu/nu(immunodeficient mice suitable for research in tumour biologyand xenografts) mice using the system illustrated in Fig. 1. Eachmouse was positioned between two angled mirrors, allowingsimultaneous imaging of three orthogonal views. Fluorescenceimages were acquired following a tail-vein injection of a mixtureof Indocyanine Green (ICG) and Dextran Texas Red (DTR). Thesetracer dyes were selected to investigate high and low molecularweights, and visible versus near-infrared imaging (see SupplementaryInformation, Fig. S1, for in vivo raw-image time series for thetwo dyes).

Data were acquired for 40 min to explore the effects of usingdifferent time windows for analysis, although as little as 20 s ofdata is sufficient for anatomical co-registration. Note also thatanatomical co-registration requires only a single suitable tracerdye, which could be delivered or induced by means of variousroutes, and could use many different configurations ofpositioning, illumination and detection. The tracer dye boluscould also be delivered after imaging of the targeted probe. Weused two dyes and multispectral, orthogonal-view dynamicimaging to demonstrate that it is possible to image the in vivodynamics of two dyes simultaneously (mimicking co-injection ofan inert anatomical tracer and a targeted probe).

Analysis of DFMI images can be approached in many ways.Figure 2 shows the results of simple principal-component analysis(PCA) on the ICG image time series acquired in two mice, onelying prone (using 5 min of data) and the other supine (using20 s of data). The spatial pixels corresponding to the second,third and fourth orthogonal temporal components of the imagetime series are visualized as red–green–blue (RGB) mergedimages (the first component is the mean image). Positive andnegative pixels are shown separately.

The structures revealed are very clearly delineated, and most aresimple to identify when compared with anatomy and dissection. Noimage segmentation was required. (See Supplementary Information,Fig. S2, for equivalent images for mouse A using only 0–20 s ofdata.) We found that different anatomical structures areemphasized when different lengths of time series are used. This isbecause the dye may clear very quickly through some organs (suchas the brain), but may take a long time to build up in others (suchas adipose tissue). In all five mice, it was possible to create ananatomical map using the image time series acquired.

Although PCA is effective in delineating structures, it is notsuitable for routine analysis. However, because ourspatiotemporal separation is based on the biodistributiondynamics of the organs themselves, these characteristics arefounded in physiology and their general trends should beconsistent and repeatable. Work is under way to fully characterizethese trends and develop a universally applicable algorithm thatuses established processing techniques from perfusion imaging21.As a first demonstration of a more generalizable approach, Fig. 3shows the result of a nine-component non-negative least-squaresfit based on a single ICG image time series. Nine basis timecourses (bone, kidney, brain, small intestine, liver, spleen, lungs,eyes/lymph nodes and adipose) were extracted from smallregions of interest, selected using the PCA images as a guide. Thefitting process then reveals the extent to which each pixel is

exhibiting each particular time course. Figure 3 shows thesecomponents colour-coded and merged (see SupplementaryInformation, Fig. S3 for individual components, and Fig. S4 fora comparison to a digital anatomical mouse atlas). This

12-bit cooledCCD camera

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filtered,shutteredwhite-light

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Figure 1 Dynamic imaging system. a,b, System for dual-dye dynamic

fluorescence molecular imaging. Two light sources and an emission-filter

changer are computer-controlled to allow time series of the biodistribution of

two dyes to be acquired simultaneously (a). The mouse is positioned between

two angled mirrors, allowing simultaneous imaging of three orthogonal views of

the same mouse (b). Note that the anatomical co-registration technique itself

only requires imaging of one dye, and could equally be achieved using a typical

two-dimensional imaging system.

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co-registered map could readily be overlaid onto simultaneously orsequentially acquired fluorescence or bioluminescence images of atargeted probe.

We have presented the first demonstration of an elegantlysimple, yet incredibly powerful technique for simultaneous, all-optical generation of a co-registered anatomical atlas for small-animal molecular imaging. The technique exploits the simple factthat each organ responds differently following injection of a dye.

Although other dyes or combinations thereof may prove equallysuitable, ICG is ideal for this application as it is clinically available,well characterized, and it excites and emits in the near infrared(NIR). The ability to emit in the NIR is the probable reason for theexcellent resolution of the organs, as light scatter is lower and tissuepenetration is higher at NIR wavelengths23. Note that a static imageof ICG would not provide any delineation of the internal organs, aseach organ does not possess specific contrast in its steady state.

All-optical anatomical co-registration may have additionaluseful properties. By providing improved estimates of the targetedprobe’s geometrical position through multiple orthogonalviews, it may be possible to improve quantitative estimation ofthe probe’s concentration24. This could be achieved by calculatinga correction factor based on the probable attenuation of light asit passes through surrounding organs on its way to and fromthe localized targeted probe.

The in vivo dynamics of inert dyes could also provide non-invasive measures of changes in the function of major organs.

For example, as ICG is used clinically to evaluate liver function25,DFMI could be used to simultaneously provide non-invasivemeasurement of the effects of a drug on the liver.

We also assert that imaging the in vivo dynamics of targetedprobes themselves (whether activatable or injected) could allowenhanced resolution and specificity by applying these samedynamic imaging techniques. Potentially valuable biodistributiondynamics have been noted in many molecular probe developmentstudies18,19 and exploitation of uptake pharmacokinetics as aquantitative measure has been demonstrated and explored26–29.Dynamic optical imaging of haemoglobin absorption in humanshas also been shown to enhance image contrast30.

To reach its full potential, optical molecular imaging in smallanimals needs to evolve into a technique capable of routine,longitudinal studies of orthotopic targets. We have demonstratedthat DFMI can provide an important contribution to this goal, asa simple and inexpensive method of simultaneously achievinganatomical co-registration irrespective of repositioning orweight gain.

METHODS

ANIMALS

Five mice (nu/nu, Massachusetts General Hospital Radiation Oncology breedingfacilities; n ¼ 5, weight ¼ 25+1 g, 6 weeks old) were anaesthetized withisoflurane, 1.25% in a 1:3 mixture of O2 and air. For optical imaging, animals

Principal component time courses

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Figure 2 In vivo anatomical maps derived using PCA of an image series following ICG injection. a,b, Principal components of images acquired for 5 min after

ICG injection (first temporal component, red; second, green; third, blue): positive (a) and negative (b) pixels. c, Corresponding PCA basis time courses. d,e, Principal

components of images acquired for 20 s after ICG injection in a second mouse that was positioned supine. f, Corresponding PCA time courses. Each organ has a

different RGB combination, and hence its own distinctive time course. Small arrows in a indicate possible lymph nodes, blue structures in b may be corresponding

lymph drainage channels. Spikes in f and ghosting in e correspond to breathing motion.

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were injected intravenously with a mixture of 0.05 ml of 260 mM ICG(Cardiogreen, Fluka) and 0.05 ml of 360 mM DTR (70,000 MW, Invitrogen) insaline (equivalent to 0.4 mg kg21 or 7.4 mM initial concentration in blood). Allanimal procedures were reviewed and approved by the Subcommittee onResearch Animal Care at Massachusetts General Hospital, where theseexperiments were performed. Mice recovered fully after imaging.

DYNAMIC OPTICAL IMAGING SYSTEM

The DFMI system was built to allow parallel multispectral dynamic molecularimaging and included an imaging bay consisting of two glass mirrors at 458 and aslightly raised central platform as shown in Fig. 1. Bifurcated liquid light guidesdelivered 9 mW of filtered white light at 570+20 nm from two positions to

excite the DTR dye. Two 785-nm laser diodes (160 mW total) deliveredlight to excite the ICG dye. A 12-bit cooled charge-coupled device (CCD) camera(Cascade, Roper Scientific) imaged the mouse through a computer-controlledemission filter-changer (Electro-Optical Products) holding a 600-nm long-passfilter, and an 850+20 nm bandpass filter. An electronic shutter in the 570-nmlight path, and digital modulation of the laser diodes allowed synchronization ofillumination with the emission filters and camera image acquisition. A series of10 images (50 ms integration time per frame) were acquired every two secondsfor each excitation/emission pair for up to 40 min after injection. For 20-s imageseries analysis, a sequence of 55 successive raw images was used (correspondingto 5 Hz frame rate imaging during periods 0–2, 4–6, 8–10, 12–14, 16–18 and20–21 s). For 5-min image series analysis, each group of 10 successive imageswas averaged to create a sequence of 75 images with four seconds between each.Image analysis was performed as described below.

IMAGE PROCESSING

Image processing and data analysis were performed using Matlab softwarefunctions including PCA (princomp) and non-negative least-squares fitting(lsqnonneg). Red–green–blue colour merging in Fig. 2 was achieved bybackground subtracting each of the three components, normalizing their peakvalues to 256, and then combining them into an unsigned 8-bit integer true-colour image. To aid visualization, a pale version of the grey-scale bright-fieldimage of the mouse taken before or after the image time series was thensuperimposed by adding it to the RGB merge and renormalizing the maximumto 256. Reading in data, PCA processing and display of 256�256 CCD imagesequences took 11 seconds on a 2 GHz Pentium M laptop with 1 Gbyte RAM.

For the nine-component image merge in Fig. 3, each organ component wasbackground subtracted and normalized to 256, and then converted to an RGBmatrix of shades of only the colour key for that organ (as shown in the time-course plot—note that ‘spleen’ was in fact white). The final image was thengenerated by adding these nine colour-coded images together. Because of this,any overlap between organs may appear as a mixture of colours that mayresemble another colour. However, there is surprisingly little overlap between theorgans (see Supplementary Information, Fig. S3, which shows each organcomponent separately).

Received 2 March 2007; accepted 3 July 2007; published 19 August 2007.

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Eye Lungs Liver Right kidney

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Figure 3 In vivo, non-invasive anatomical mapping of nine organ-specific

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AcknowledgementsThis work was funded by National Institutes of Health grants: 1R01DK072137, 5R01DK064850,R21DK071225 and 1U54CA126513. The authors wish to sincerely thank R. M. Levenson and X. E. Guofor helpful discussions and guidance. We also acknowledge the contributions of S. B. Raymond,B. J. Bacskai, M. Bouchard and D. A. Boas at Massachusetts General Hospital.Correspondence and requests for materials should be addressed to E.M.C.H.Supplementary Information accompanies this paper on www.nature.com/naturephotonics

Author contributionsE.M.C.H. conceived the technique, designed and performed the experiments, analysed the data and wrotethe manuscript. A.M. prompted and guided development of the concept and aided in data acquisition,data interpretation and manuscript preparation.

Competing financial interestsThe authors declare competing financial interests: details accompany the full-text HTML version of thepaper at www.nature.com/naturephotonics

Reprints and permission information is available online at http://npg.nature.com/reprintsandpermissions/

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Supplemental figures for

All-optical anatomical coregistration for small animal molecular imaging using dynamic contrast.

Elizabeth M. C. Hillman

1 and Anna Moore

2

1Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, 1210

Amsterdam Avenue, New York, NY 10027. 1-212-854-2788, eh2245@columbia.edu.

2Molecular Imaging Laboratory, MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts

General Hospital, Building 149, 13th

Street, Charlestown, MA 02129

Figure S1. Raw timecourse images of indocyanine green and dextran texas red following simultaneous injection

Figure S2. In-vivo anatomical maps derived using PCA of a 20 second image series following ICG injection

Figure S3. In-vivo optical spatiotemporal separation of nine ‘organs’ using basis timecourses (ICG only).

Figure S4. Comparison of DFMI anatomical image with digital anatomical atlas.

© 2007 Nature Publishing Group

Figure S1. Raw timecourse images of indocyanine green and dextran texas red following

simultaneous injection. Left: Raw data series of ICG and Texas red from injection to 2 minutes post-

injection. Mouse is prone. Each image is normalized to its own maximum. Right: Timecourses of

selected regions are shown for nominally identified organs. Top row shows absolute timecourse,

bottom row shows time-course divided by the mean time-course. Note high initial fluorescence in large

intestine at texas-red wavelengths (corresponding to food autofluorescence). Note the rapid transit of

ICG in the lungs (as the direct route from the tail vein to the right ventricle to the pulmonary artery).

© 2007 Nature Publishing Group

Figure S2. In-vivo anatomical maps derived using PCA of an image series following ICG

injection (same as figure 2 in main text, but for first 20 seconds of mouse A). Left: maps of the 2nd

, 3rd

and 4th

temporal principal components of images acquired during the first 20 seconds after ICG

injection (color-coded red, green and blue). The mouse is in a prone position. Right: The

corresponding PCA basis timecourses of the 1st – 4

th components of the image series. Each identified

organ has a different red-green-blue combination, and hence each has its own distinctive timecourse.

The spikes in the 4th component correspond to breathing-related motion. Labeled organs were

identified by comparison with post-mortem dissection and general anatomy.

© 2007 Nature Publishing Group

Figure S3. In-vivo optical spatiotemporal separation of nine ‘organs’ using basis timecourses

(ICG only). The individual spatial maps resulting from a non-negative least-squares fit of 9 basis

timecourses to a 26 minute long image series (these are the individual elements of the image

composition shown in figure 3 of the main text). Fit results are shaded cyan and overlaid onto a feint

bright-field image. In most cases, localization to one place is achieved. Additional structure indicates

that these regions also have similar dynamic behavior to the primary region.

© 2007 Nature Publishing Group

Figure S4. Comparison of DFMI anatomical image with digital anatomical atlas. Top: image from

figure 3 in main test. Bottom: Digimouse anatomical atlas http://neuroimage.usc.edu/Digimouse.html

red = liver, blue = spleen, purple = kidney, green = heart, pink = brain, cyan = lungs (pale blue =

bladder, yellow = testes [digimouse is male, dynamouse is female]).