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1 March 2008 Use of scaling relations to extract intrinsic fluorescence lifetime of targets embedded in turbid media
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We present a novel method for estimating the intrinsic fluorescence lifetime of deeply embedded localized fluorophores. It is based on scaling relations, characteristic for turbid media. The approach is experimentally substantiated by successfully reconstructing lifetimes for targets at depths up to 14.5 mm. A derived correction factor was determined from the product of the transport-corrected scattering coefficient μs and the index of refraction nr. In addition, data from an array of detectors (≥2) can be used to estimate μs nr. The suggested algorithm is a promising tool for diagnostic fluorescence, since lifetime can be a sensitive indicator of the fluorophore environment.

Due to recent advances in the development of molecular probes, fluorescence is increasingly being used to analyze biological processes at the molecular and cellular levels. The high specificity of fluorescence imaging explains the considerable interest in this modality by the biomedical community. Fluorescent markers conjugated with corresponding antibodies can target diseased cells, creating a promising tool for medical diagnostics. However, existing applications of fluorescence in biology and medicine are mainly limited to surface and/or thin tissue sample imaging.1, 2 Advances in new fluorescent agents with higher quantum yields, operating in the near-IR spectral window, are favorable for the penetration of light into biological tissue and have stimulated new efforts in fluorescent imaging of deeply embedded targets. To make this modality quantitative, photon migration models that take into account the turbid nature of biological tissues should be developed.3, 4, 5

Optical imaging data can be collected by three schemes: continuous wave (cw), time domain, or frequency domain. To choose the most suitable system, the trade-off between the cost of the system and measurable information should be taken into account. Low-cost cw imaging enables one to measure only 2-D intensity distributions of fluorescent light that can be used to map distributions of the fluorophores, while frequency-domain and especially time-domain measurements can provide much richer informational content, in particular, related to fluorescence lifetime distributions. Fluorescence lifetime information can be used to track possible alterations of physiological parameters (e.g., pH and temperature) resulting from malignant transformation of healthy tissue, because time-resolved spectroscopy of some specifically targeted fluorescent labeled antibodies is known to be sensitive to such variations. Changes in the physiological status of tissues can be observed by comparing these parameters inside an abnormality (or very close to it) to that of surrounding healthy tissue. Thus, a high-resolution functional image of the region of interest can be provided, which is important for early cancer diagnostics.

To take advantage of these rapidly improving experimental techniques, adequate inverse algorithms to reconstruct spatial and temporal characteristics of the deeply embedded fluorophores are required. The general inverse problem of diffuse optical tomography, including fluorescence tomography, is ill-posed, making it computationally expensive while reducing the accuracy of quantifying the optical parameters. For this reason, alternative approaches have been discussed in the literature.4 In general, they use analytical formulas obtained from photon migration models to derive lifetimes of deeply embedded targets from the time-of-flight distributions of fluorescent photons. The random walk model6, 7 was applied to acquire closed-form expressions for deeply embedded fluorophores with relatively short lifetimes, when the time delay of photons between source and detector due to scattering was higher than the fluorescence lifetime. Kumar, 4 considering an opposite case of a lifetime longer than the diffusive time scale, presented conditions under which the asymptotic behavior of emitted intensity reveals the intrinsic lifetime of the fluorophore. More recently, a theoretical analysis elucidated the diffuse and pure fluorescent decay components of diffuse time-resolved fluorescence.3 Using the Born approximation to the transport equation, the authors derived more general expressions for time-resolved fluorescence tomography, including the case of a low-absorbing medium, when the asymptotic approach fails and the entire time-resolved distribution should be used. The authors verified their approach with tomographic reconstruction of simulated data. However, the low signal-to-noise ratio (SNR) that is expected in the long-time tail of the distribution is potentially a problem for data analysis.

The goal of this study was to find and experimentally verify scaling relations that could be used to correct observed time-resolved intensity distributions from fluorescent targets at a given depth z inside a turbid medium to an expected surface distribution (from the same fluorophore, but at z=0 ), revealing the intrinsic fluorescence lifetime without the necessity for full-scale reconstruction. Similar corrections could be applied when comparing the time-resolved data obtained from the same deeply embedded fluorophore by several detectors positioned at different distances from the source (excitation photon entry point into the medium). This research focuses on the shape of the corresponding time-resolved curve, important lifetime estimates, not on amplitude scaling.

The basis for this approach is a diffusion-like photon migration model, either the random walk theory or the diffusion equation. According to these models, the dependence of time-resolved reflectance on the source-detector separations r is limited to an exponential factor exp[(3μsnrr24ct)] , where t is the photon time of flight, and c is the speed of light in a vacuum (see, e.g., Ref. 8). Thus, the ratio of reflected intensities for source-detector separations r and zero is

Eq. 1

Strictly speaking, Eq. 1 implies the same background optical parameters (scattering and absorption) of the medium at excitation and emission wavelengths λex and λem . Under this condition the ratio Γe(t,r)Γe(t,0) does not depend on absorption because for the same time of flight, it results in the same attenuation in the numerator and denominator.

Making the preceding assumption [and Eq. 1] is justified if the selected values of λex and λem are close and provide nonsignificant absorption variations in the medium (scattering coefficient changes), described by a power law, and are expected to be small for (λemλex)λex1 . Practically, one can use the average value of scattering coefficient μs=(μs,ex+μs,em)2 in Eq. 1.

In an isotropic medium with the same-source-detector geometry, we can expect a similar relationship between time-resolved fluorescence curves from a surface fluorophore and one embedded at depth z :

Eq. 2

The factor 4 in the denominator of the exponent index is absent because a photon travels the distance z twice (first as an excitation photon and then as an emission photon to the detector).

To verify the introduced correction factors, we used a recently developed time-resolved imaging system (details are given in Ref. 9). Data are collected by 2-D scanning of the region of interest (ROI) by a linear fiber array with fixed separations between a single source fiber and several detection fibers. The impulse response function (IRF) width in our experiments was 0.35ns . Our simulations show that it may introduce an error of <1% into estimates of fluorescence lifetime (τ> 0.7ns) . To assess the performance of the instrument, tissue-like highly scattering phantoms were created with a fluorescent target placed at different depths inside the medium. These phantoms were prepared from an agarose-based gel, containing a small target microbeads of Alexa Fluor 750 (Invitrogen Inc., California, USA) in water solution. The fluorescence excitation/emission wavelengths were 750790nm . Total absorption coefficients of many human tissues in vivo (e.g., breast) are close at these wavelengths10 (2μa750nmμa790nm)(μa750nm+μa790nm)10% , justifying our approach for further biomedical applications of similar dyes.

To simulate the scattering properties of tissues, 10% Intralipid solution was added to the phantom gel for a final lipid concentration of 0.85%. Based on the literature, expected values11, 12 were nr1.4 and μs2.1(0.852)mm10.89mm1 for λ=780nm . Phantom dimensions were sufficient to provide semiinfinite geometry with respect to the fluorescent target. The fluorophore depth z was varied by adding slabs of phantom material placed above the original surface. A zero time shift t09.9ns was introduced in the experimental setup to center the observed curves in the 25-ns measurement window. The experimental setup used for the model verification is very basic: the source fiber at the sample surface and two detection fibers at given distances from the source (source-detector separations r1 and r2 , respectively). For simplicity, we considered experimental data with the excitation source fiber placed directly above a single fluorophore, though our general conclusions concerning scaling relations in Eqs. 1, 2 do not change.

A series of experiments with different target depths z=0 to 14.5mm clearly demonstrated the dependence of the apparent fluorescence lifetime—determined as an observed exponential decay time of emission intensity I(t):τ={dln[I(t)]dt}1 —on the fluorophore depth. Due to light diffusion deeper embedded fluorophores showed longer apparent lifetimes (not to be confused with intrinsic fluorescence lifetime, determined by the fluorophore itself and its immediate environment).

Time-resolved data from a fluorophore at z=0 , 14.5mm are presented in Fig. 1 . The difference in the slopes of long time tails of the two distributions can be clearly seen (compare with simple asymptotic model of Ref. 4, where similar slopes were expected). This corresponds to observed lifetimes of τ00.72ns for z=0mm and τ0.89ns for z=14.5mm . For comparison note that the lifetime of Alexa Fluor 750 in water solution reported by the manufacturer ( is τn0.7nsτ0 (our measurements gave τn0.68ns ). Using Eq. 2 with known μs0.9mm1 and nr1.4 to modify the measured distribution for z=14.5mm , we can compare the expected distribution at the sample surface with the observed value. Excluding amplitude scaling, which can be seen as a constant shift in logarithmic scale, the agreement is good. In the broad time range 10.5nst15.5ns both dependencies ln[I(t)] are close to linear (linear regression correlation coefficient r20.99 ) with practically the same slopes (1.39 versus 1.41ns1 ). This fact demonstrates the possibility to estimate intrinsic lifetime from the time-resolved data obtained from a deeply embedded fluorophore. Similar results were obtained for several other depths z=6 to 13.8mm (data not shown).

Fig. 1

Time-resolved intensities of two fluorophores with depths z . The corrected data show the closeness of the reconstructed lifetime to the intrinsic lifetime.


A comparison of the data collected from the same fluorophore at z=14.5mm by different fibers further substantiates our model. Figure 2 presents time-resolved distributions, measured by two fibers at source-detector separations r1 and r2 of 2 and 5mm . Transformation of data from the first detector, according to Eq. 1 with the same μs and nr but different source-detector separation r2 predicts the expected shape of the distribution at the second detector. The discrepancy between the predicted and measured distributions (excluding constant amplitude scaling) is small (<5%) over the broad range 10.5nst15.5ns .

Fig. 2

Time-resolved data obtained from the fluorophore by detectors with source-detector separations of 2 and 5mm , respectively.


If two time-resolved data sets from the same target at different positions inside the medium are available, they can be used to estimate the product μsnr from Eqs. 1, 2. For example, by analyzing the linear regression of ln[I(t)z=0I(t)z=14.5mm] relative to 1(tt0) , one can find the slope (3z2c)μsnr2.66 , corresponding to μs0.9mm1 , if nr=1.4 (see Fig. 3 ). A similar estimate of μs , but with greater error due to smaller difference in distances, can be obtained by comparing signals at two detectors (not presented).

Fig. 3

Linear regression of intensities ratio (in logarithmic scale) for two depths as a function of 1(tt0) .


The results of our analysis show that the simple scaling relationships of Eqs. 1, 2 enable one to estimate the intrinsic fluorescence lifetime of a deeply embedded small fluorophore without full-scale reconstruction if the product μsnr as well as the relative positions of the source, target, and detector are known. We can estimate μsnr if two data sets with known geometrical setups are available (the simplest case is the same fluorophore and two detectors with significantly different source-detector separations). As for the fluorophore position, it can be estimated independently, opening the way for lifetime reconstruction. Lateral coordinates can be found by scanning the sample surface to search for the maximum emitted intensity (e.g., Ref. 13). Several algorithms to estimate target depth have been suggested in the literature: for time-resolved data, considering position of the fluorescence temporal point-spread function (TPSF) maximum14 and the mean time of the fluorescent curves,15 and for cw data analyzing the width of 2-D intensity distribution.13, 16

In summary, we suggest a multistep analysis of time-resolved data on deeply embedded fluorescent targets to estimate intrinsic fluorescence lifetime, avoiding the necessity to solve an ill-posed inverse problem:

  • 1. Localize the target in three dimensions. The lateral coordinates correspond to a maximum intensity of emitted light, found by scanning of the region of interest; the depth is determined either from the maximum of the TPSF, the mean time of flight, or cw intensity distributions.

  • 2. Analyze data obtained from several source-detector separations, using Eq. 1 to estimate μsnr (as was recently presented in Ref. 12) and compare results for several source-detector pairs, verifying the validity of medium uniformity assumption.

  • 3. Introduce the correction of Eq. 2 into the observed lifetime intensity distribution to determine the intrinsic fluorescence lifetime from the obtained “surface” intensity distribution.

The accuracy of our approach depends on the accuracy of 3-D localization of the fluorophore, and to a lesser extent, on an error in our preliminary knowledge of μsnr . Since the relative positions of the source and detector are given, and the fluorophore lateral coordinates are estimated straightforwardly from the surface intensity distribution (e.g., Ref. 17), the main concern is the sensitivity of the lifetime estimate to assumed fluorophore depth. According to Refs. 15, 16, 17, expected errors in the depth are 10% . To simulate potential errors in depth estimate, we replaced the genuine z value of z=14.5mm by z=13.2 or 16mm , introducing the 10% depth error into our analysis. After modified correction, the emission intensity distribution reveals lifetimes of 769 and 662ns , respectively, instead of 719ns , corresponding to real z=14.5mm (deviation of 7 to 8%). Note that the lifetime estimate from the noncorrected data of 885ns is much higher (23%) . Note that for the suggested correction factor, an error of 20% in μsnr is equivalent to an error 10% in z .

In the case of several fluorescent targets, we should first localize them (in the case of two fluorophores in the ROI, this can be done, for example, by curve fitting of cw data, as described in Refs. 13, 17). For the case of the fluorescent layers considered, for example, in Ref. 5 the necessary corrections to the observed decay time of emitted light can be found by integrating Eqs. 1, 2 over lateral coordinates of the fluorophore (x,y) .

Note that although this paper discusses reflectance geometry, a similar approach using basic scaling relations for photon migration should be applicable for transmission geometry. We are currently considering its applications for small animal fluorescent imaging.


The authors acknowledge funding by the Intramural Research Program of National Institute of Child Health and Human Development of the National Institutes of Health.



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©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
Victor V. Chernomordik, Moinuddin Hassan, Franck Amyot, Jason D. Riley, and Amir H. Gandjbakhche "Use of scaling relations to extract intrinsic fluorescence lifetime of targets embedded in turbid media," Journal of Biomedical Optics 13(2), 024025 (1 March 2008).
Published: 1 March 2008


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