Improvement of fluorescence-enhanced optical tomography with improved optical filtering and accurate model-based reconstruction algorithms.
ABSTRACT The goal of preclinical fluorescence-enhanced optical tomography (FEOT) is to provide three-dimensional fluorophore distribution for a myriad of drug and disease discovery studies in small animals. Effective measurements, as well as fast and robust image reconstruction, are necessary for extensive applications. Compared to bioluminescence tomography (BLT), FEOT may result in improved image quality through higher detected photon count rates. However, background signals that arise from excitation illumination affect the reconstruction quality, especially when tissue fluorophore concentration is low and/or fluorescent target is located deeply in tissues. We show that near-infrared fluorescence (NIRF) imaging with an optimized filter configuration significantly reduces the background noise. Model-based reconstruction with a high-order approximation to the radiative transfer equation further improves the reconstruction quality compared to the diffusion approximation. Improvements in FEOT are demonstrated experimentally using a mouse-shaped phantom with targets of pico- and subpico-mole NIR fluorescent dye.
- SourceAvailable from: Chinmay Darne[Show abstract] [Hide abstract]
ABSTRACT: The work presented herein describes the system design and performance evaluation of a miniaturized near-infrared fluorescence (NIRF) frequency-domain photon migration (FDPM) system with non-contact excitation and homodyne detection capability for small animal fluorescence tomography. The FDPM system was developed specifically for incorporation into a Siemens micro positron emission tomography/computed tomography (microPET/CT) commercial scanner for hybrid small animal imaging, but could be adapted to other systems. Operating at 100 MHz, the system noise was minimized and the associated amplitude and phase errors were characterized to be ±0.7% and ±0.3°, respectively. To demonstrate the tomographic ability, a commercial mouse-shaped phantom with 50 µM IRDye800CW and (68)Ga containing inclusion was used to associate PET and NIRF tomography. Three-dimensional mesh generation and anatomical referencing was accomplished through CT. A third-order simplified spherical harmonics approximation (SP(3)) algorithm, for efficient prediction of light propagation in small animals, was tailored to incorporate the FDPM approach. Finally, the PET-NIRF target co-localization accuracy was analyzed in vivo with a dual-labeled imaging agent targeting orthotopic growth of human prostate cancer. The obtained results validate the integration of time-dependent fluorescence tomography system within a commercial microPET/CT scanner for multimodality small animal imaging.Physics in Medicine and Biology 11/2012; 57(24):8135-8152. · 2.70 Impact Factor
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ABSTRACT: Non-invasive injectable cellular therapeutic strategies based on sustained delivery of physiological levels of BMP-2 for spinal fusion are emerging as promising alternatives, which could provide sufficient fusion without the associated surgical risks. However, these injectable therapies are dependent on bone formation occurring only at the specific target region. In this study, we developed and deployed fluorescence gene reporter tomography (FGRT) to provide information on in vivo cell localization and viability. This information is sought to confirm the ideal placement of the materials with respect to the area where early bone reaction is required, ultimately providing three dimensional data about the future fusion. However, because almost all conventional fluorescence gene reporters require visible excitation wavelengths, current in vivo imaging of fluorescent proteins is limited by high tissue absorption and confounding autofluorescence. We previously administered fibroblasts engineered to produce BMP-2, but is difficult to determine 3-D information of placement prior to bone formation. Herein we used the far-red fluorescence gene reporter, IFP1.4 to report the position and viability of fibroblasts and developed 3-D tomography to provide placement information. A custom small animal, far-red fluorescence tomography system integrated into a commercial CT scanner was used to assess IFP1.4 fluorescence and to demark 3-D placement of encapsulated fibroblasts with respect to the vertebrae and early bone formation as assessed from CT. The results from three experiments showed that the placement of the materials within the spine could be detected. This work shows that in vivo fluorescence gene reporter tomography of cell-based gene therapy is feasible and could help guide cell-based therapies in preclinical models.Optics Express 10/2013; 21(20):24129-24138. · 3.55 Impact Factor
Improvement of fluorescence-enhanced
optical tomography with improved optical
filtering and accurate model-based
John C. Rasmussen
Eva M. Sevick-Muraca
Journal of Biomedical Optics 16(12), 126002 (December 2011)
Improvement of fluorescence-enhanced optical
tomography with improved optical filtering and
accurate model-based reconstruction algorithms
Yujie Lu, Banghe Zhu, Chinmay Darne, I-Chih Tan, John C. Rasmussen, and Eva M. Sevick-Muraca
University of Texas Health Science Center at Houston, Center for Molecular Imaging, Institute of Molecular Medicine,
1825 Pressler Street, SRB 330A, Houston, Texas 77030
Abstract. The goal of preclinical fluorescence-enhanced optical tomography (FEOT) is to provide three-
dimensional fluorophore distribution for a myriad of drug and disease discovery studies in small animals. Ef-
fective measurements, as well as fast and robust image reconstruction, are necessary for extensive applications.
Compared to bioluminescence tomography (BLT), FEOT may result in improved image quality through higher
detected photon count rates. However, background signals that arise from excitation illumination affect the re-
construction quality, especially when tissue fluorophore concentration is low and/or fluorescent target is located
deeply in tissues. We show that near-infrared fluorescence (NIRF) imaging with an optimized filter configuration
significantly reduces the background noise. Model-based reconstruction with a high-order approximation to the
radiative transfer equation further improves the reconstruction quality compared to the diffusion approximation.
Improvements in FEOT are demonstrated experimentally using a mouse-shaped phantom with targets of pico- and
subpico-mole NIR fluorescent dye.C ?2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3659291]
Keywords: fluorescence-enhanced optical tomography; excitation light rejection; simplified spherical harmonics approximations; finite
element methods; reconstruction algorithm; parallel computation.
Paper 11484R received Sep. 6, 2011; revised manuscript received Oct. 17, 2011; accepted for publication Oct. 18, 2011; published
online Dec. 14, 2011.
Fluorescence-enhanced optical tomography (FEOT) can be
confounded by effects of autofluorescence and a high “noise
floor,” which arises from excitation light leakage through
optical rejection filters. This high noise floor can obscure
signals from low-concentration fluorophores in tissues and
impact reconstruction quality. Because tissue autofluorescence
peaks at visible wavelengths [typically, for example, the peak of
mouse skin is 500 nm (Ref. 1)] and exponentially reduces with
increase of wavelength, excitation in near-infrared (NIR) FEOT
(excitation wavelengths ≥750 nm) effectively removes the
confounding artifact of tissue autofluorescence.2Nonetheless,
NIR FEOT is not immune to high noise floors owing to exci-
tation light leakage. The performance deterioration of optical
filters, including reduced and blueshifted optical densities of
the interference filters, occurs when scattered excitation light is
incident at non-normal directions.3In addition to accurate mea-
surements, model-based reconstruction methods require precise
mathematical models to describe photon propagation and
generation in tissues. Although the radiative transfer equation
(RTE) is the choice of method, it is complicated and can impose
severe time constrains for directly obtaining solutions in com-
plex geometries as required for a rodent. Although the diffusion
approximation (DA) has been extensively applied in optical
tomography at its early stages in development, it becomes
increasingly inaccurate in small volumes (such as a mouse)
and under conditions of high absorption (such as in the rodent
Address all correspondence to: Eva M. Sevick-Muraca, University of Texas Health
Science Center at Houston, Center for Molecular Imaging, Institute of Molecular
Medicine, 1825 Pressler Street, SRB 330A, Houston, Texas 77030; Tel: 713-500-
3560; Fax: 713-500-0319; E-mail: email@example.com
In this paper, we demonstrate that (i) experimental mea-
surements optimized by simple filter configurations to reduce
background signals and (ii) accurate models of light propaga-
tion together improve NIR FEOT. The measurement sensitivity
and overall quality of NIR FEOT begins with the detector. Over
the past decade, our group developed an intensified charge-
coupled device (ICCD) camera system to realize frequency-
domain, time-dependent, and continuous wave noncontact fluo-
filter and one 830-nm bandpass filter [Fig. 1(a)] were used to
sion photons from human subjects under noninvasive imaging
conditions following microdosing (<100 and >10 μg of ICG
administrated to humans).4Herein, we employed an optimized
filter configuration, that is two 830-nm bandpass filters located
before and after a 28-mm NIKKOR focusing lens [Fig. 1(b)]
to further reduce background signal owing to excitation light
Using both configurations,phantomFEOT studies were con-
ducted using a mouse-shaped solid phantom (Caliper Life Sci-
ences, Hopkinton, Massachusetts). The scattering and absorp-
tion coefficients, anisotropic factor, and refractive factor of the
phantom were taken to be 9.5 mm−1, 0.0066 mm−1, 0.9, 1.5
at 785 nm and 7.4 mm−1, 0.0077 mm−1, 0.9, 1.5 at 830 nm,
respectively as provided by the manufacturer. ICG of 2 μmol/l,
0.5 μmol/l, and 0.125 μmol/l was sealed in plastic volumes
of 2.35 mm3, and total molar quantities range from 4.7 to
1083-3668/2011/16(12)/126002/4/$25.00 C ?2011 SPIE
Journal of Biomedical OpticsDecember 2011rVol. 16(12)126002-1
Lu et al.: Improvement of fluorescence-enhanced optical tomography...
Fig. 1 (a) Unoptimized and (b) optimized filter configurations in the
gain-modulated intensified detection system.
0.29 pmol. The absorption coefficient of ICG was measured
(Spectrophotometer DU800, Beckman Coulter, Brea, Califor-
nia), and for fluorescent targets in the phantom, we computed
the absorption ratio of fluorophore to surrounding tissue (AR)
to range 29.54–0.51. The ICG targets were then placed into
a predrilled hole within the mouse phantom, and a rod com-
prised of the same material as the phantom was used to fill the
remaining volume of the hole. Excitation light of 8.1 mW first
transmission fluorescence measurements were taken onthe ven-
fluorescence measurements were taken on the dorsal surface.
The exposure times were fixed at 800 ms, and the gain of the
intensifier (the voltage relevant to the gain ranges from 6.51 to
8.77 V) was adjusted to reach equivalent maximum fluorescent
counts for each view. It is noteworthy that the increase of the
gain does not affect the detected photon distribution, although it
improves the sensitivity and reduces detectable dynamic range.
Using the simplified spherical harmonics approximation
(SPN),5,6we previously developed a fully parallel linear recon-
struction algorithm for bioluminescence tomography (BLT).7
In the algorithm developed herein, the reconstruction is signifi-
cantly accelerated using parallel implementation in the cluster.
The linear reconstruction framework is easily used in the com-
bination of multispectral and multiview measurements to im-
prove the reconstruction quality. In addition, we also extend our
parallel reconstruction framework for NIR FEOT. In this recon-
struction algorithm, we solve the following linear least-squares
a) : ?Aμsf
where J+, m, bis the measurable exiting partial current for emis-
sion (W mm−2); μsf
is the absorption coefficient (mm−1)
of the fluorophore; μsf,sup
is the upper bound constraint;
and A denotes the linear relationship between J+, m, band
multiple excitations are used in reconstruction, J+, m, band
A consist of [J+,m,b
Ai,...,ANm]T, where Nmdenotes the total number of emission
measurements and T is a transpose operator. In order to acquire
Ai, we use similar methods found in the literature7for the SP3
a . When multiple emission measurements obtained from
SP3equation when the finite element method is used and Bmis
obtained by its components bm
where ? is the domain for reconstruction; r is the location in
?; vp, qare the shape functions; Q is the quantum efficiency of
the fluorophore; and φxis the fluence of the excitation and is
obtained by directly solving the SP3excitation approximation
when omitting the absorption coefficient of the fluorophore.
After a series of matrix deductions from Eq. (2), Ai can be
obtained.7Because of the ill-posed nature of A, several factors,
such as measurement noise and mathematical model errors, sig-
ods have become popular to reduce the effect. In this work, we
need to evaluate the effect of mathematical models in the recon-
struction. Therefore, regularization term is not used in Eq. (1).
Figures 2(a) and 2(b) show the measured surface emission
photon distribution from the dorsal and ventral projections, re-
spectively, when the inclusion contained 4.7 pmol of ICG and
two 830 nm filters were used. The profiles extracted along the
lines shown in the top panels of Fig. 2 are shown in Figs. 2(c)
(dorsal) and 2(d) (ventral) for the data from different filters and
between the intensifier gain and the count number on the cam-
era and measurement noise, it is difficult to make the maximum
maximal count number for comparison. One can find that the
photondistribution changeswith the reductionof the target con-
to the dorsal side than the ventral side, the changes of the mea-
sured photon distribution on the latter are more distinct because
tected. Note that the normalized intensity profiles arising from
different target ICG concentrations and measured using the op-
timized filter configuration are more similar as compared to the
counterparts from unoptimized filter configurations, showing
the effectiveness of excitation light leakage rejection.
and position of the fluorescent target. The volumetric mesh of
the phantom was generated for the reconstruction using the
Amira 5.0 software (Mercury Computer Systems, Inc., Chelms-
points. Using a similar registration method in the literature,8the
measured surface emission distribution and incident excitation
light were mapped onto the surface of the volumetric mesh.
Reconstructions were performed on a cluster of eight nodes (8
CPU coresof 3.0GHzand16GB RAMat eachnode),and3000
data points (about 1300 and 1700 for the ventral and dorsal
sides, respectively) were used in reconstruction and reconstruc-
tion iteration number was set to 3000. Figures 3 and 4 show
the results from the DA- and SP3-based reconstruction, respec-
tively. The DA-based reconstruction time reduced from 108.0
to 19.0 min, when the number of the CPU cores used increased
from 1 to 45. It is noteworthy that the SP3-based reconstruction
failed on one single node with one CPU core due to memory
time was 30.0 min, close to the time required for the DA-based
jis the submatrix corresponding to ϕm
jin the i-th
Journal of Biomedical OpticsDecember 2011rVol. 16(12)126002-2
Lu et al.: Improvement of fluorescence-enhanced optical tomography...
Fig. 2 Surface emission photon distribution measured from the (a) dorsal and (b) ventral projections when 4.7 pmol of ICG comprised the target
within the mouse phantom. (c) and (d), corresponding to (a) and (b) respectively, are the photon distribution comparisons between different ARs,
molar quantities, and filters. The profiles were through the peak of the emission photon distribution along the surface line shown in the top panels.
reconstruction, and showed good performance of the fully
parallel reconstruction framework. Because of the noise factors,
there are some reconstructed artifacts, as shown in Figs. 3 and
4. However, when the maximum reconstructed values were
used to localize the fluorophore target, localization errors of the
Fig. 3 DA-based reconstruction comparisons between different filter
configurations at varying target fluorophore molar quantities and ARs.
Cross sections with thick and thin boundaries are the center positions
of the actual (70.0, 65.5, 5.5) and reconstructed fluorophore targets
(maximal values), respectively. The volumetric mesh denotes the top
80% of the contour levels of reconstructed fluorophore distribution.
“Error” and “Dist.” denote relative errors (x, y, z in column) and dis-
tance (in column) between the actual and reconstructed positions.
“Arti. Yes” denotes whether there are artifacts inside the phantom.
SP3-based reconstruction were found to be smaller than those
obtained from DA, as shown in Figs. 3 and 4. When 0.29 pmol
of ICG and 785and830-nmfilters were used,the position of the
ICG target was not localized using either of DA- and SP3-based
results due to very large errors. However, when we used the
optimized filter combination and SP3-based reconstruction, we
obtained better localization of the target containing 0.29 pmol
In FEOT, the localization accuracy of the fluorophore target
is decided by the photon distribution on the tissue surface and
model-based reconstruction. In noncontact collection mode
Fig. 4 SP3-based reconstruction comparisons between different filters,
molar quantities, and ARs. The display settings are the same as those
in Fig. 3.
Journal of Biomedical OpticsDecember 2011rVol. 16(12) 126002-3
Lu et al.: Improvement of fluorescence-enhanced optical tomography...
with moderately large fields of view where the optical filtering
components are subjected to light with large incident angles,
excitation light leakage has significant effect on the sensitivity
of fluorescence detection and tomographic reconstruction.
Because of the blueshifted characteristics, the single 785-nm
notch filter in the unoptimized detection scheme did not
effectively reject excitation light leakage. When the excitation
photon leakage is comparable to the fluorescence signal as in
the case of the fluorophore with low molar quantities and ARs,
the reduction of the detection performance becomes distinct.
In the optimized detection system, the 830-nm bandpass
filter after the lens effectively reduces the blueshifted effect
because of the collection of the focused light. In addition,
because of the interference phenomena between two adjoining
filters, the performance of two bandpass filters cannot be
improved with a direct sum of their optical density (OD).
Some loss materials are needed between filters to reduce the
multiple-path interference. The focus lens plays this role,
improving the performance of the combined filters. Although
another potential solution to remove excitation light leakage is
to obtain excitation photon distribution from the same settings
before and after the fluorophore is injected, such an approach
complicates the experiments since gain settings are not known
until after fluorescent agent is injected. The approach becomes
impossible when gene-encoded fluorescent reporters are used.
With the optimized optical filtering, improved localization is
obtained from the SP3-based image reconstruction. The recon-
struction time is significantly reduced from the fully parallel
reconstruction framework. The improved FEOT is particularly
important for targeted molecular imaging as tissue disease
markers are typically present in pico- to femto-molar quantities
with low target-to-tissue absorption ratios. Although the tissue
our results have shown that sub-pmol molar quantities of ICG
with a little more than half the absorption of the surrounding
tissues can be reconstructed using the improved FEOT. The
work shows the potential of FEOT in the future applications.
This work is supported by NIH Grants No. R01CA135673 and
No. U54CA136404, and a training fellowship from the Keck
Center Computational Cancer Biology Training Program of the
Gulf Coast Consortia (CPRIT Grant No. RP101489).
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