AD Klose

Arion F. Chatziioannou

C Kuo

David Stout

DL Prout

G Alexandrakis

G Wang

H Dehghani

Harvey Herschman

Hidevaldo B. Machado

J Virostko

JR Mansfield

Lu Yujie

P Ray

Qinan Bao

V Ntziachristos

W Cong

W Han

Y Lu

Yujie Lu

English

PubMed

Springer - Publisher Connector

In Vivo Mouse Bioluminescence Tomography with Radionuclide-Based Imaging Validation

Yujie  Lu

Crossref

B The Author(s), 2010Published Online: 13 May 2010 DOI: 10.1007/s11307-010-0332-yMol Imaging Biol (2011) 13:53Y58RESEARCH ARTICLEIn Vivo Mouse Bioluminescence Tomographywith Radionuclide-Based Imaging ValidationYujie Lu,1 Hidevaldo B. Machado,2 Qinan Bao,1 David Stout,1 Harvey Herschman,1,2Arion F. Chatziioannou11Crump Institute for Molecular Imaging, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA,Los Angeles, CA 90095, USA2Department of Biological Chemistry, Molecular Biology Institute, University of California, Los Angeles, CA 90095, USAAbstractIntroduction: Bioluminescence imaging, especially planar bioluminescence imaging, has beenextensively applied in in vivo preclinical biological research. Bioluminescence tomography (BLT)has the potential to provide more accurate imaging information due to its 3D reconstructioncompared with its planar counterpart.Methods: In this work, we introduce a positron emission tomography (PET) radionuclideimaging-based strategy to validate the BLT results. X-ray computed tomography, PET,spectrally resolved bioluminescence imaging, and surgical excision were performed on a tumorxenograft mouse model expressing a bioluminescent reporter gene.Results: With different spectrally resolved measured data, the BLT reconstructions were acquiredbased on the third-order simplified spherical harmonics (SP3) approximation and the diffusionapproximation (DA). The corresponding tomographic images were obtained for validation ofbioluminescence source reconstruction.Conclusion: Our results show the strength of PET imaging compared with other validationmethods for BLT and improved source localization accuracy based on the SP3 approximationcompared with the diffusion approximation.Key words: Optical molecular imaging, Bioluminescence imaging/tomography, Imaging validation,Reconstruction algorithmIntroductionOptical molecular imaging, especially planar biolumi-nescence and fluorescence imaging, has been exten-sively applied in preclinical research, particularly with smallanimal models such as genetically modified mice andmurine tumor xenografts. Tomographic optical molecularimaging localizes the source position and should providemore accurate biological information compared to its planarcounterpart [1]. The possibility and potential of biolumines-cence tomography (BLT) [2, 3] and fluorescence moleculartomography as standalone imaging modalities have beeninvestigated using phantoms and mouse experiments [4, 5].To develop a practical, accurate, and robust BLT system,more factors need to be investigated, such as in vivovalidation strategies and more precise reconstruction algo-rithms among others. To validate the optical sourceinformation, physical and in vitro methods are commonlyused in phantom- and nonbiological mouse-based experi-ments. In vivo imaging strategies, such as computedtomography (CT), are also used to validate artificialsource-based optical experiments [6]. Due to the poor softtissue contrast in preclinical CT imaging, it is difficult toidentify the optical source in vivo based on anatomicalinformation only. New in vivo validation strategies arenecessary to develop tomographic optical molecular imaging.In the transition from phantom-based feasibility investigationsCorrespondence to: Arion F. Chatziioannou; e-mail: archatziioann@mednet.ucla.eduto in vivo biological research, several assumptions affectsource reconstruction quality [7]. Although the diffusionapproximation (DA) has been widely used, the methodbecomes inaccurate when the bioluminescence sources arenear the surface, or in tissues with high and anisotropicabsorption [7, 8]. This inaccuracy leads to reduced informa-tion acquired from bioluminescence sources. Reconstructionalgorithms based on high-order approximations to theradiative transfer equation (RTE) should improve these resultsand need to be further developed [8]. In addition, a prioriinformation, such as spectrally resolved measurements [9, 10]and permissible source region [11], is indispensable toconstrain the possible solutions in BLT reconstruction.In this paper, an in vivo validation strategy based on positronemission tomography (PET) is proposed for tomographicbioluminescence imaging. An in vivo mouse experiment wasperformed with a luciferase-based tumor xenograft. Afteracquiring multiple spectral optical data, the bioluminescencesource was localized with SP3- and DA-based reconstructionalgorithms and compared to PET and CT acquired data. Thereconstructed results not only show the advantages of PET-based in vivo validation compared with traditional CT-basedmethods but also demonstrate the effectiveness and potential ofthe SP3-based BLT reconstruction algorithm.Materials and MethodsIn the experiment, three types of imaging modalities, that is, X-rayCT, PET, and optical imaging, were used. To maintain the animalin the same position throughout the whole procedure, a glass holderwas fabricated to support the mouse. To realize multi-viewdetection, two mirrors were used to acquire the photon distributionfrom two side views. A murine tumor cell line, MC38fluc,transfected to provide constitutive expression of firefly luciferase,was used to generate a tumor xenograft in the abdomen of a nudemouse and allowed to develop for 3 weeks. To perform the imagingexperiments, the animal was anesthetized and injected with theoptical substrate (luciferin). Optical data was gathered 10 min aftersubstrate administration using the Maestro 2 in vivo ImagingSystem (CRI, Woburn, MA, USA). The filter bandpass width wasset to 20 nm, and optical data at four wavelengths (600, 620, 640,and 660 nm) was collected. The exposure time for each wavelengthwas 5 min. Fig. 1a shows a mouse photograph and thecorresponding optical data at 660 nm. The image signal-to-noise ratio (SNR) ranged from 6.81 to 7.27 (SNR was calculatedby Savg/Navg at four wavelengths, where Savg and Navg are theaverages of the image signal and noise). Since the optical signalsare weak and the sensitivity of this particular detection system islow, the image SNR is low. However, the nature of the optical filter[12] allowed the photon distribution on the mouse surface to beobserved for arbitrary wavelength bands. Because the tumorposition after 3 weeks was very close to the mouse abdomen, thephoton distribution could not be acquired from the two side views.After finishing the bioluminescence optical signal acquisition, thePET tracer (18F-fluorodeoxy-glucose (18F-FDG)) was injectedintravenously into the mouse. After 1 h uptake, the animal wasimaged using a microCT and a high resolution preclinical PETsystem (Siemens Preclinical Solutions, Knoxville, TN, USA) toobtain the CT and PET images shown in Fig. 1b, c. Localization ofthe tumor position from the PET images is facilitated due to thehigh 18F-FDG uptake compared to background. However, the sameis not true when analyzing the CT images due to the similar densitycontrast between the tumor and other tissues in the animalabdomen. These data clearly show the advantages of PET imagingfor in vivo optical imaging validation. After all the collection of theimaging data, the mouse was euthanized and dissected to confirmthe tumor location. A tumor mass of approximately 3 mm diameterwas found attached to the small intestine, shown in Fig. 1d. Torealize the spectrally resolved BLT reconstruction with the high-order approximations to the RTE, we have developed a fullyparallel reconstruction framework with the simplified sphericalharmonics approximation (SPN) [13]. Regarding the SP3 approx-imation, its mathematical model and boundary formula are [14]r 13a1 r’1 þ a’1  23 a ’2 ¼ S 23 a ’1 r 17a3 r’2 þ 49 a þ 59 a2 ’2 ¼  23 S(ð1ÞFig. 1. Imaging of a xenograft tumor in a nude mouse. The photograph is used to map the optical data from the CCD cameraonto the surface of the volumetric mesh; the computed tomography slice shows the same position cross-section with thepositron emission tomography scan; dissection is used to further confirm the tumor position.54 Y. Lu et al.: In vivo Mouse BLT with PET-based Validation1þB13a1 ur’1  D1a3 ur’2 ¼  12 þ A1 ’1 þ 18 þ C1 ’2 D2a1 ur’1 þ 1þB27a3 ur’2 ¼ 18 þ C2 ’1  724 þ A2 ’28<:ð2Þwhere μan=μs(1−gn)+μa(n=1, 2, 3), φi(i=1, 2) are the compositemoments relevant to the Legendre moments, μs and μa are thescattering and absorption coefficients, and S is the bioluminescencesource. Note that μan, φi, and S depend on the wavelength whenspectrally resolved measured data is acquired. The coefficientsA1,...,D1,...,A2,...,D2 can be calculated using the formulas in [14].Furthermore, the exiting partial current J+ on the mouse surface isobtained:Jþ;b ¼ 14þ J0 ’1  23 ’2  0:5þ J13a1 ur’1þ 13516þ J2 ’2  J37a3 ur’2ð3Þwhere the coefficients J0,...,J3 can also be calculated based on therelevant formulas in [14]. Note that SP1 (DA) can be obtainedcorrespondingly by setting φ2=0. After a series of deductions, asimple relationship between the measurable boundary data and theunknown source distribution is established:Jþ;b ¼ ASp ð4ÞwhereJþ;b ¼Jþ;b l1ð Þ...Jþ;b lkð Þ...Jþ;b lKð Þ2666666437777775;A ¼g1G l1ð Þ...gkG lkð Þ...gKG lKð Þ2666666437777775where K is the number of the used wavelengths, γk is the percentageat the wavelength λk of the total energy, and G(λk) is therelationship matrix between the permissible source region Sp andthe measurable data J+,b at the wavelength λk. The surfacemeasured data J+,m corresponding to J+,b leads to a reconstructionfailure when solving Eq. 4 directly due to the presence of noise. Wecan solve the bound-constrained least squares problemmin0GSpGSsup Spð Þ : ASp  Jþ;mj jj j2 ð5Þwhere Ssup is the upper bound of the source density. By minimizingthe objective function Θ(Sp), BLT reconstruction becomes possible.Here, the limited memory variable metric bound-constrained quasi-Newton method (BLMVM) is used for BLT reconstruction [15].ResultsWith respect to the photon distribution on the mouse surface,the volumetric mesh of half the mouse body shown in Fig. 2was generated using the commercial software Amira 3.0(Mercury Computer Systems, Inc. Chelmsford, MA, USA).Since the photon distribution can only be obtained from theventral view and the photon propagation path is almosttotally consisted of muscle tissue, we assumed that thereconstructed domain was homogeneous with muscle tissue.Fig. 2. The volumetric mesh used in bioluminescence tomography reconstruction and the photon distribution at 660 nm. Thered lines are used to show the permissible source region.Table 1. Optical properties of actual mouse muscle [7]Wavelength 600 nm 620 nm 640 nm 660 nmμa(λ) (mm−1) 0.187 0.107 0.088 0.08μs(λ) (mm−1) 9.29 9.22 9.13 9.02Y. Lu et al.: In vivo Mouse BLT with PET-based Validation 55The mesh contained about 10,000 discretized points with theaverage element of 1.2 mm diameter. The assumed opticalproperties of mouse muscle are shown in Table 1. Toconstrain the BLT solution, a permissible source region with5,965 discretized points was selected, that is Sp={(x,y,z)|33.0GyG55.0 mm, (x,y,z) ∈ Ω}, where Ω is the entiredomain, as shown in Fig. 2. The acquired optical data wasmapped onto the mesh surface after manual co-registrationbetween the mesh and the mouse photograph in Amira. Thedifferences of the optical properties at different wavelengthsSP3-basedDA-basedFig. 3. Imaging validation and bioluminescence tomography reconstruction comparison between SP3- and DA-based models.The dashed lines are used to align the boundaries of optical cross-sections with the corresponding computed tomographyslices; the red lines show the center of tumor on positron emission tomography images.Fig. 4. DA- and SP3-based bioluminescence tomography reconstruction comparisons with different spectrally resolvedmeasurements. 1W, 2W, and 4W denotes that one (660 nm), two (600 and 660 nm), and four wavelengths are used. Cross-sections with blue and red boundaries are the center position of the actual and reconstructed sources, respectively. Thevolumetric mesh denotes reconstructed values larger than 10% of the reconstructed maximum.56 Y. Lu et al.: In vivo Mouse BLT with PET-based Validationimprove the BLT reconstruction quality. Since the opticalproperties at 600 and 660 nm have a large difference (asshown in Table 1), Fig. 3 shows the SP3- and DA-basedreconstruction results when using 1,072 measured points andthese two wavelengths. Through comparison betweenoptical and PET reconstructed results, one can determinethat the source location errors of SP3-based reconstructionare less than 1.5 mm at each direction. However, the DA-based BLT reconstruction is not as accurate. In the depthdirection, the location errors are about 4 mm. Note that thebackground noise is very high due to nonspecific probeuptake in PET imaging. This reconstruction comparisonshows the effectiveness of the SP3-based reconstructionalgorithm for BLT. To further confirm the effect ofspectrally resolved measured data, BLT reconstructionsusing measurements in one and four wavelengths wereperformed. The reconstructed results are shown in Fig. 4.Due to the absence of source depth information when usingone wavelength, poor reconstruction results were obtained.However, when four wavelengths were used, the localizationof the source center was not improved; instead, there weresome artifacts in the reconstructed results compared withtwo wavelengths. Small differences in the optical propertiesbetween 620, 640, and 660 nm reduced the benefits frommultispectral measured data, while the noise effects in themeasured data became significant. Although the use ofmultispectral data can improve reconstructed image quality,in principle, the results are a trade-off between photonsensitivity (presence of statistical noise) and differencesbetween optical properties.Discussions and ConclusionTo the best of the authors' knowledge, this contributionrepresents the first time that PET imaging has been used forin vivo BLT imaging validation. The results show theeffectiveness of radionuclide imaging and the potential ofhigh-order approximation models for BLT reconstruction.Further research will focus on mouse experiments withdisease models. In using BLT with these models, validationwith FDG-PET may not always be possible if the targettissue does not demonstrate adequate image contrast. Forthis purpose, we will be utilizing genetically modified mousemodels in which target tissues for bioluminescent imagingare expressing both a bioluminescence-based reporter geneand a PET probe/PET reporter gene [16].Although the simulated and experimental [17] SPN-basedBLT reconstruction algorithm provides improved local-ization precision of the bioluminescent source, the sen-sitivity of the detection system of BLT plays a veryimportant role in BLT reconstruction for experimentalreconstructions. With the increase of the tumor depth, theoptical signals on the surface of the mouse are signifi-cantly attenuated. More sensitive detection systems becomenecessary to improve image reconstruction. A new Optical-PET (OPET) imaging system [18] provides not only thesimultaneous detection of optical signals and gamma raysbut also higher sensitivity due to the natural multi-viewdetection mode and the photon collection from larger solidangle. More experiments will be performed on the OPETsystem, and relevant results will be reported in the future,especially on the effect of signal loss, image reconstruction,and sensitivity for the methodology.Acknowledgments. We thank Dr. Laurent Bentolila for use of the Maestro 2system. We are grateful to Judy Edwards and Waldemar Ladno for theirassistance with mouse experiments. This work is supported by the NIBIBR01-EB001458, a NIH/NCI 2U24 CA092865 cooperative agreement, DOEDE-SC0001234, and NCI 5-R01 CA08572.Open Access. This article is distributed under the terms of the CreativeCommons Attribution Noncommercial License which permits anynoncommercial use, distribution, and reproduction in any medium,provided the original author(s) and source are credited.References1. Ntziachristos V, Ripoll J, Wang LV, Weisslder R (2005) Looking andlistening to light: the evolution of whole body photonic imaging. NatBiotechnol 23(3):313–3202. Wang G, Li Y, Jiang M (2004) Uniqueness theorems in biolumines-cence tomography. Med Phys 31(8):2289–22993. Han W, Cong W, Wang G (2006) Mathematical theory andnumerical analysis of bioluminescence tomography. Inverse Probl22:1659–16754. Ntziachristos V, Tung C-H, Bremer C, Weissleder R (2002) Fluorescence-mediated tomography resolves protease activity in vivo. Nat Med 8(7):757–7605. Kuo C, Coquoz O, Troy TL, Xu H, Rice BW (2007) Three-dimensionalreconstruction of in vivo bioluminescent sources based on multispectralimaging. J Biomed Opt 12:0240076. Wang G, Cong W, Durairaj K, Qian X, Shen H, Sinn P, Hoffman E,McLennan G, Henry M (2006) In vivo mouse studies with bio-luminescence tomography. Opt Express 14:7801–78097. Virostko J, Powers AC, Duco Jansen E (2007) Validation ofluminescent source reconstruction using single-view spectrally resolvedbioluminescence images. Appl Opt 46:2540–25478. Klose AD (2007) Transport-theory-based stochastic image reconstruc-tion of bioluminescent sources. J Opt Soc Am A 24:1601–16089. Dehghani H, Davis SC, Jiang S, Pogue BW, Paulsen KD, Patterson MS(2006) Spectrally resolved bioluminescence optical tomography. OptLett 31:365–36710. Alexandrakis G, Rannou FR, Chatziioannou AF (2005) Tomographicbioluminescence imaging by use of a combined optical-PET (OPET)system: a computer simulation feasibility study. Phys Med Biol50:4225–424111. Cong W, Wang G, Kumar D, Liu Y, Jiang M, Wang LV, Hoffman EA,McLennan G, McCray PB, Zabner J, Cong A (2005) Practicalreconstruction method for bioluminescence tomography. Opt Express13(18):6756–677112. Mansfield JR, Sowa MG, Mantsch HH (2000) Development of LCTF-based visible and near-IR spectroscopic imaging systems for macro-scopic samples. Proc SPIE 3920:99–10713. Yujie Lu, Machado HB, Douraghy A, Stout D, Herschman H,Chatziioannou AF (2009) Experimental bioluminescence tomographywith fully parallel radiative-transfer-based reconstruction framework.Opt Express 17:16681–1669514. Klose AD, Larsen EW (2006) Light transport in biological tissue basedon the simplified spherical harmonics equations. J Comput Phys 220(1):441–470Y. Lu et al.: In vivo Mouse BLT with PET-based Validation 5715. Benson SJ, Moré J (2001) A limited-memory variable-metric algorithmfor bound-constrained minimization. Technical Report ANL/MCS-P909-0901, Mathematics and Computer Science Division, ArgonneNational Laboratory, Argonne16. Ray P, De A, Min J-J, Tsien RY, Gambhir SS (2004) Imaging tri-fusionmultimodality reporter gene expression in living subjects. Cancer Res64:1323–133017. Lu Y, Douraghy A, Machado HB, Stout D, Tian J, Herschman H,Chatziioannou AF (2009) Spectrally resolved bioluminescence tomog-raphy with the third-order simplified spherical harmonics approxima-tion. Phys Med Biol 54:6477–649318. Prout DL, Silverman RW, Chatziioannou AF (2004) Detector conceptfor OPET—a combined PET and optical imaging system. IEEE TransNucl Sci 51(3):752–75658 Y. Lu et al.: In vivo Mouse BLT with PET-based Validation

B The Author(s), 2010
Published Online: 13 May 2010 DOI: 10.1007/s11307-010-0332-y
Mol Imaging Biol (2011) 13:53Y58
RESEARCH ARTICLE
In Vivo Mouse Bioluminescence Tomography
with Radionuclide-Based Imaging Validation
Yujie Lu,1 Hidevaldo B. Machado,2 Qinan Bao,1 David Stout,1 Harvey Herschman,1,2
Arion F. Chatziioannou1
1Crump Institute for Molecular Imaging, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA,
Los Angeles, CA 90095, USA
2Department of Biological Chemistry, Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
Abstract
Introduction: Bioluminescence imaging, especially planar bioluminescence imaging, has been
extensively applied in in vivo preclinical biological research. Bioluminescence tomography (BLT)
has the potential to provide more accurate imaging information due to its 3D reconstruction
compared with its planar counterpart.
Methods: In this work, we introduce a positron emission tomography (PET) radionuclide
imaging-based strategy to validate the BLT results. X-ray computed tomography, PET,
spectrally resolved bioluminescence imaging, and surgical excision were performed on a tumor
xenograft mouse model expressing a bioluminescent reporter gene.
Results: With different spectrally resolved measured data, the BLT reconstructions were acquired
based on the third-order simplified spherical harmonics (SP3) approximation and the diffusion
approximation (DA). The corresponding tomographic images were obtained for validation of
bioluminescence source reconstruction.
Conclusion: Our results show the strength of PET imaging compared with other validation
methods for BLT and improved source localization accuracy based on the SP3 approximation
compared with the diffusion approximation.
Key words: Optical molecular imaging, Bioluminescence imaging/tomography, Imaging validation,
Reconstruction algorithm
Introduction
Optical molecular imaging, especially planar biolumi-nescence and fluorescence imaging, has been exten-
sively applied in preclinical research, particularly with small
animal models such as genetically modified mice and
murine tumor xenografts. Tomographic optical molecular
imaging localizes the source position and should provide
more accurate biological information compared to its planar
counterpart [1]. The possibility and potential of biolumines-
cence tomography (BLT) [2, 3] and fluorescence molecular
tomography as standalone imaging modalities have been
investigated using phantoms and mouse experiments [4, 5].
To develop a practical, accurate, and robust BLT system,
more factors need to be investigated, such as in vivo
validation strategies and more precise reconstruction algo-
rithms among others. To validate the optical source
information, physical and in vitro methods are commonly
used in phantom- and nonbiological mouse-based experi-
ments. In vivo imaging strategies, such as computed
tomography (CT), are also used to validate artificial
source-based optical experiments [6]. Due to the poor soft
tissue contrast in preclinical CT imaging, it is difficult to
identify the optical source in vivo based on anatomical
information only. New in vivo validation strategies are
necessary to develop tomographic optical molecular imaging.
In the transition from phantom-based feasibility investigations
Correspondence to: Arion F. Chatziioannou; e-mail: archatziioann@
mednet.ucla.edu
to in vivo biological research, several assumptions affect
source reconstruction quality [7]. Although the diffusion
approximation (DA) has been widely used, the method
becomes inaccurate when the bioluminescence sources are
near the surface, or in tissues with high and anisotropic
absorption [7, 8]. This inaccuracy leads to reduced informa-
tion acquired from bioluminescence sources. Reconstruction
algorithms based on high-order approximations to the
radiative transfer equation (RTE) should improve these results
and need to be further developed [8]. In addition, a priori
information, such as spectrally resolved measurements [9, 10]
and permissible source region [11], is indispensable to
constrain the possible solutions in BLT reconstruction.
In this paper, an in vivo validation strategy based on positron
emission tomography (PET) is proposed for tomographic
bioluminescence imaging. An in vivo mouse experiment was
performed with a luciferase-based tumor xenograft. After
acquiring multiple spectral optical data, the bioluminescence
source was localized with SP3- and DA-based reconstruction
algorithms and compared to PET and CT acquired data. The
reconstructed results not only show the advantages of PET-
based in vivo validation compared with traditional CT-based
methods but also demonstrate the effectiveness and potential of
the SP3-based BLT reconstruction algorithm.
Materials and Methods
In the experiment, three types of imaging modalities, that is, X-ray
CT, PET, and optical imaging, were used. To maintain the animal
in the same position throughout the whole procedure, a glass holder
was fabricated to support the mouse. To realize multi-view
detection, two mirrors were used to acquire the photon distribution
from two side views. A murine tumor cell line, MC38fluc,
transfected to provide constitutive expression of firefly luciferase,
was used to generate a tumor xenograft in the abdomen of a nude
mouse and allowed to develop for 3 weeks. To perform the imaging
experiments, the animal was anesthetized and injected with the
optical substrate (luciferin). Optical data was gathered 10 min after
substrate administration using the Maestro 2 in vivo Imaging
System (CRI, Woburn, MA, USA). The filter bandpass width was
set to 20 nm, and optical data at four wavelengths (600, 620, 640,
and 660 nm) was collected. The exposure time for each wavelength
was 5 min. Fig. 1a shows a mouse photograph and the
corresponding optical data at 660 nm. The image signal-to-
noise ratio (SNR) ranged from 6.81 to 7.27 (SNR was calculated
by Savg/Navg at four wavelengths, where Savg and Navg are the
averages of the image signal and noise). Since the optical signals
are weak and the sensitivity of this particular detection system is
low, the image SNR is low. However, the nature of the optical filter
[12] allowed the photon distribution on the mouse surface to be
observed for arbitrary wavelength bands. Because the tumor
position after 3 weeks was very close to the mouse abdomen, the
photon distribution could not be acquired from the two side views.
After finishing the bioluminescence optical signal acquisition, the
PET tracer (18F-fluorodeoxy-glucose (18F-FDG)) was injected
intravenously into the mouse. After 1 h uptake, the animal was
imaged using a microCT and a high resolution preclinical PET
system (Siemens Preclinical Solutions, Knoxville, TN, USA) to
obtain the CT and PET images shown in Fig. 1b, c. Localization of
the tumor position from the PET images is facilitated due to the
high 18F-FDG uptake compared to background. However, the same
is not true when analyzing the CT images due to the similar density
contrast between the tumor and other tissues in the animal
abdomen. These data clearly show the advantages of PET imaging
for in vivo optical imaging validation. After all the collection of the
imaging data, the mouse was euthanized and dissected to confirm
the tumor location. A tumor mass of approximately 3 mm diameter
was found attached to the small intestine, shown in Fig. 1d. To
realize the spectrally resolved BLT reconstruction with the high-
order approximations to the RTE, we have developed a fully
parallel reconstruction framework with the simplified spherical
harmonics approximation (SPN) [13]. Regarding the SP3 approx-
imation, its mathematical model and boundary formula are [14]
r 13a1 r’1 þ a’1  23 a ’2 ¼ S
 23 a
 
’1 r 17a3 r’2 þ 49 a þ 59 a2 ’2 ¼  23 S
(
ð1Þ
Fig. 1. Imaging of a xenograft tumor in a nude mouse. The photograph is used to map the optical data from the CCD camera
onto the surface of the volumetric mesh; the computed tomography slice shows the same position cross-section with the
positron emission tomography scan; dissection is used to further confirm the tumor position.
54 Y. Lu et al.: In vivo Mouse BLT with PET-based Validation
1þB1
3a1
 
ur’1  D1a3
 
ur’2 ¼  12 þ A1 ’1 þ 18 þ C1 ’2
 D2a1
 
ur’1 þ 1þB27a3
 
ur’2 ¼ 18 þ C2 ’1  724 þ A2 ’2
8<
:
ð2Þ
where μan=μs(1−gn)+μa(n=1, 2, 3), φi(i=1, 2) are the composite
moments relevant to the Legendre moments, μs and μa are the
scattering and absorption coefficients, and S is the bioluminescence
source. Note that μan, φi, and S depend on the wavelength when
spectrally resolved measured data is acquired. The coefficients
A1,...,D1,...,A2,...,D2 can be calculated using the formulas in [14].
Furthermore, the exiting partial current J+ on the mouse surface is
obtained:
Jþ;b ¼ 1
4
þ J0
 
’1  23 ’2
 
 0:5þ J1
3a1
 
ur’1
þ 1
3
5
16
þ J2
 
’2  J37a3
 
ur’2
ð3Þ
where the coefficients J0,...,J3 can also be calculated based on the
relevant formulas in [14]. Note that SP1 (DA) can be obtained
correspondingly by setting φ2=0. After a series of deductions, a
simple relationship between the measurable boundary data and the
unknown source distribution is established:
Jþ;b ¼ ASp ð4Þ
where
Jþ;b ¼
Jþ;b l1ð Þ
..
.
Jþ;b lkð Þ
..
.
Jþ;b lKð Þ
2
6666664
3
7777775
;A ¼
g1G l1ð Þ
..
.
gkG lkð Þ
..
.
gKG lKð Þ
2
6666664
3
7777775
where K is the number of the used wavelengths, γk is the percentage
at the wavelength λk of the total energy, and G(λk) is the
relationship matrix between the permissible source region Sp and
the measurable data J+,b at the wavelength λk. The surface
measured data J+,m corresponding to J+,b leads to a reconstruction
failure when solving Eq. 4 directly due to the presence of noise. We
can solve the bound-constrained least squares problem
min0GSpGSsup S
pð Þ : ASp  Jþ;mj jj j2 ð5Þ
where Ssup is the upper bound of the source density. By minimizing
the objective function Θ(Sp), BLT reconstruction becomes possible.
Here, the limited memory variable metric bound-constrained quasi-
Newton method (BLMVM) is used for BLT reconstruction [15].
Results
With respect to the photon distribution on the mouse surface,
the volumetric mesh of half the mouse body shown in Fig. 2
was generated using the commercial software Amira 3.0
(Mercury Computer Systems, Inc. Chelmsford, MA, USA).
Since the photon distribution can only be obtained from the
ventral view and the photon propagation path is almost
totally consisted of muscle tissue, we assumed that the
reconstructed domain was homogeneous with muscle tissue.
Fig. 2. The volumetric mesh used in bioluminescence tomography reconstruction and the photon distribution at 660 nm. The
red lines are used to show the permissible source region.
Table 1. Optical properties of actual mouse muscle [7]
Wavelength 600 nm 620 nm 640 nm 660 nm
μa(λ) (mm
−1) 0.187 0.107 0.088 0.08
μs(λ) (mm
−1) 9.29 9.22 9.13 9.02
Y. Lu et al.: In vivo Mouse BLT with PET-based Validation 55
The mesh contained about 10,000 discretized points with the
average element of 1.2 mm diameter. The assumed optical
properties of mouse muscle are shown in Table 1. To
constrain the BLT solution, a permissible source region with
5,965 discretized points was selected, that is Sp={(x,y,z)|
33.0GyG55.0 mm, (x,y,z) ∈ Ω}, where Ω is the entire
domain, as shown in Fig. 2. The acquired optical data was
mapped onto the mesh surface after manual co-registration
between the mesh and the mouse photograph in Amira. The
differences of the optical properties at different wavelengths
SP
3
-based
DA-based
Fig. 3. Imaging validation and bioluminescence tomography reconstruction comparison between SP3- and DA-based models.
The dashed lines are used to align the boundaries of optical cross-sections with the corresponding computed tomography
slices; the red lines show the center of tumor on positron emission tomography images.
Fig. 4. DA- and SP3-based bioluminescence tomography reconstruction comparisons with different spectrally resolved
measurements. 1W, 2W, and 4W denotes that one (660 nm), two (600 and 660 nm), and four wavelengths are used. Cross-
sections with blue and red boundaries are the center position of the actual and reconstructed sources, respectively. The
volumetric mesh denotes reconstructed values larger than 10% of the reconstructed maximum.
56 Y. Lu et al.: In vivo Mouse BLT with PET-based Validation
improve the BLT reconstruction quality. Since the optical
properties at 600 and 660 nm have a large difference (as
shown in Table 1), Fig. 3 shows the SP3- and DA-based
reconstruction results when using 1,072 measured points and
these two wavelengths. Through comparison between
optical and PET reconstructed results, one can determine
that the source location errors of SP3-based reconstruction
are less than 1.5 mm at each direction. However, the DA-
based BLT reconstruction is not as accurate. In the depth
direction, the location errors are about 4 mm. Note that the
background noise is very high due to nonspecific probe
uptake in PET imaging. This reconstruction comparison
shows the effectiveness of the SP3-based reconstruction
algorithm for BLT. To further confirm the effect of
spectrally resolved measured data, BLT reconstructions
using measurements in one and four wavelengths were
performed. The reconstructed results are shown in Fig. 4.
Due to the absence of source depth information when using
one wavelength, poor reconstruction results were obtained.
However, when four wavelengths were used, the localization
of the source center was not improved; instead, there were
some artifacts in the reconstructed results compared with
two wavelengths. Small differences in the optical properties
between 620, 640, and 660 nm reduced the benefits from
multispectral measured data, while the noise effects in the
measured data became significant. Although the use of
multispectral data can improve reconstructed image quality,
in principle, the results are a trade-off between photon
sensitivity (presence of statistical noise) and differences
between optical properties.
Discussions and Conclusion
To the best of the authors' knowledge, this contribution
represents the first time that PET imaging has been used for
in vivo BLT imaging validation. The results show the
effectiveness of radionuclide imaging and the potential of
high-order approximation models for BLT reconstruction.
Further research will focus on mouse experiments with
disease models. In using BLT with these models, validation
with FDG-PET may not always be possible if the target
tissue does not demonstrate adequate image contrast. For
this purpose, we will be utilizing genetically modified mouse
models in which target tissues for bioluminescent imaging
are expressing both a bioluminescence-based reporter gene
and a PET probe/PET reporter gene [16].
Although the simulated and experimental [17] SPN-based
BLT reconstruction algorithm provides improved local-
ization precision of the bioluminescent source, the sen-
sitivity of the detection system of BLT plays a very
important role in BLT reconstruction for experimental
reconstructions. With the increase of the tumor depth, the
optical signals on the surface of the mouse are signifi-
cantly attenuated. More sensitive detection systems become
necessary to improve image reconstruction. A new Optical-
PET (OPET) imaging system [18] provides not only the
simultaneous detection of optical signals and gamma rays
but also higher sensitivity due to the natural multi-view
detection mode and the photon collection from larger solid
angle. More experiments will be performed on the OPET
system, and relevant results will be reported in the future,
especially on the effect of signal loss, image reconstruction,
and sensitivity for the methodology.
Acknowledgments. We thank Dr. Laurent Bentolila for use of the Maestro 2
system. We are grateful to Judy Edwards and Waldemar Ladno for their
assistance with mouse experiments. This work is supported by the NIBIB
R01-EB001458, a NIH/NCI 2U24 CA092865 cooperative agreement, DOE
DE-SC0001234, and NCI 5-R01 CA08572.
Open Access. This article is distributed under the terms of the Creative
Commons Attribution Noncommercial License which permits any
noncommercial use, distribution, and reproduction in any medium,
provided the original author(s) and source are credited.
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Cong A

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Development of LCTFbased visible and near-IR spectroscopic imaging systems for macroscopic samples.

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Mathematical theory and numerical analysis of bioluminescence tomography.

Spectrally resolved bioluminescence optical tomography.

Spectrally resolved bioluminescence tomography with the third-order simplified spherical harmonics approximation.

Three-dimensional reconstruction of in vivo bioluminescent sources based on multispectral imaging.

Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study.

Transport-theory-based stochastic image reconstruction of bioluminescent sources.

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Validation of luminescent source reconstruction using single-view spectrally resolved bioluminescence images.

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In Vivo Mouse Bioluminescence Tomography with Radionuclide-Based Imaging Validation

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