Fluorescence Contrast in Tissue

Fluorescence represents a ubiquitous means of achieving optical molecular contrast using diverse instruments including spectrophotometers, microarrays, microscopes, and endoscopes. Fluorescence measurements can provide information not only on the specific molecular makeup of a sample but also on the local environment surrounding the fluorescence molecule or fluorophore. Distinct species of fluorophores are characterized based on their excitation and emission spectra, their quantum efficiency, their polarization and their fluorescence lifetime.28,61,84,99,125 Most common endogenous fluorophores in biological tissue used for tissue characterization include aromatic amino acids (tyrosine, tryptophane, and phenylalanine), structural proteins (elastin, collagens and collagen cross-links), enzyme metabolic co-factors [nicotinamide adenine (phosphate) dinucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD)], lipid components and porphyrins. The absorption and emission characteristics of these fluorophores have been extensively studied and reviewed in numerous publications.8,17,28,61,84,99,125 Alternatively, fluorescence measurements in tissue can take advantage of a broad range of exogenous molecular probes8,54 with well-defined fluorescent characteristics. This includes a few probes approved for human use by the U.S. Food and Drug Administration (FDA) such as fluorescein and indocyanine green (ICG)8,54 and porphyrin-based photosensitizers used in phototherapy.1,27,41,95,103,110 This article focuses primarily on the use of tissue autofluorescence contrast as means of in vivo tissue characterization and diagnosis.

Fluorescence in Tissue Characterization and Diagnosis

For a long time the measurement of tissue autofluorescence has being used as a means for the investigation of the molecular properties of biological cells and tissue as it has the potential to infer information about biochemical, functional and structural transformations of fluorescent bio-molecular complexes in these biological entities. Since such transformations can occur as a result of pathological transformation, therapeutic intervention1,9,17,20,22,28,41,61,84,99,125 or during development,5,32,37,97 fluorescence has been increasingly explored as a tool for tissue diagnosis. Moreover, the advances in light delivery and collection systems via fiber optics have facilitated the development of fluorescence-based techniques for non- or minimally invasive, remote investigations of tissues using endoscopic or catheter systems compatible with clinical application.123 A wide range of applications of fluorescence techniques to tissue diagnostic, ranging from a variety of cancers to cardiovascular diseases and bioengineered tissues, were reported and presented in several pertinent publications.1,5,8,9,17,20,22,28,32,37,84,97,99,125 Example of representative reviews of fluorescence-based studies in medicine include those by Wagnieres et al.,125 Bigio and Mourant,9 Andersson-Engels et al.,1 Richards-Kortum and Sevick-Muraca,99 Marcu,68 and Ramanujam.97

Autofluorescence based contrast has been of particular interest in clinical research studies as the use of endogenous fluorescence for tissue characterization do not require the administration of a contrast agent. Both steady-state (intensity and/or spectrum) and time-resolved (time-domain or frequency-domain) fluorescence measurements (Fig. 1) have been employed for retrieving quantitative and qualitative information with respect to compositional makeup and pathophysiology of biological tissues. Table 1 presents a summary of the majority of autofluorescence studies4,10–13,15,16,19,21,24–26,31,33–36,40,42,43,45,46,48,49,52,53,55,59,63,65,66,72–76,79,82,85,87,89–92,94,96,98,101,102,104,106–109,112,117,119,120,122,124,137–139 conducted in vivo in patients over the past 12 years. These studies have demonstrated the broad applicability of autofluorescence spectroscopy and imaging techniques for the diagnosis of tissue pathologies.

Figure 1
figure 1

(a) Spectro-temporal profile of fluorescence emission in response to an excitation light stimuli; (b) Emission spectrum depicting the time-integrated intensities; (c) fluorescence pulse transient for a particular emission wavelength

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Table 1 Human in vivo studies for disease detection using endogenous fluorescence
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The steady-state technique, characterized typically by relative simple implementation and rather inexpensive instrumental setups, has been the most explored as a clinical tool for the diagnosis of diseased tissues in vivo in patients. In addition to experimental prototype systems engineered in individual research laboratories, commercial systems are nowadays available for autofluorescence measurements in tissues. For example, Xillix Technologies Corp (currently Novadaq Technologies Inc.),128 Karl Storz,129 Pentax,130 and Richard Wolf131 have commercialized such endoscopic systems LIFE, D-light, SAFE-100, and DAFE, respectively. These systems are equipped with a fluorescence excitation-collection module and designed to analyze autofluorescence contrast to diagnose cancers in the bronchi and gastrointestinal (GI) tract. Medispectra Inc. and SpectRx (currently Guided Therapeutics)132 have developed devices incorporating fluorescence and reflectance spectroscopy for diagnosis of cancer of the cervix. Also, relatively recently the FDA has approved a new device VELscope (LED Dental, Inc.133) for direct visualization of autofluorescence of oral cavity and diagnosis of oral carcinoma. The reported sensitivity of many of these devices for detecting specific diseases stages is high, but their specificity remains limited.14,84,122 This is due to a number of factors emerging from the difficulty to conduct absolute emission intensity measurements in vivo. The fluorescence intensity measurements are affected by changes in light excitation-collection geometries due to tissue movement, presence and change in concentration of endogenous absorbers (e.g., hemoglobin), potential non-uniform sample illumination due to variation in tissue surface profile, and photobleaching effects. To account for these limiting factors, in many situations the steady–state fluorescence measurements are accompanied by either diffuse reflectance spectroscopy (DRS) measurements in order to account for hemoglobin absorption or by rigorous calibration procedures designed to correct for potential factors that non-linear affect the measured fluorescence intensity values.11,31,33,48,53,65,66,72,82,87,90,96,98,106,120,139

The alternative method to measure fluorescence, time-resolved (lifetime) technique, can address the limitations of the steady-state technique by resolving the dynamics of fluorescence decay; thus capitalizing on the additional dimension of fluorescence emission—the excited state lifetime(s). This approach also improves the specificity of fluorescence measurements.8,17,28,99 Conceptually, the fluorescence lifetime is the average time a fluorophore spends in the excited states following excitation from its ground energy level. As many of the fluorophores in biological tissues have an overlapping spectra,17,84,125 the fluorescence lifetime properties can provide a contrast parameter. Also, the fluorescence lifetime varies with the molecular environment (e.g., pH, pO2 values) and the fluorescence decay time is usually independent of fluorophore concentration and its quantum yield.17,28,61 In brief, the use of time-resolved fluorescence for studying biological systems offers several distinct advantages including a means: (a) to resolve biomolecules with overlapping fluorescence emission spectra but with different fluorescence decay times; (b) to account for quenching of the fluorescence by change of the physicochemical environment as the measurements are sensitive to various parameters of the biological microenvironment (including pH, ion concentration and binding, enzymatic activity, temperature, redox state); (c) for more robust quantitative measurements in vivo since the lifetime measurements are independent of intensity.

Despite these recognized inherent advantages, the full potential value of fluorescence lifetime information has not been extensively evaluated in clinical settings due to several barriers. This includes the complexity of the instrumental setup, the lengthy data acquisition and analysis, and the high instrumentation cost. As clearly demonstrated by the listing of reports depicted in Table 1 only a fraction of the total clinical research studies in patients in vivo are based on time-resolved techniques. Numerous time-resolved fluorescence studies were conducted in excised tissue specimens,17,20,22,28,68,84 however the outcome of these studies cannot be fully extrapolated to in vivo measurements.68,71,72 This is due to changes in tissue biochemical composition related to functional properties (e.g., metabolism, hypoxia, etc.) as well as changes in tissue morphology generated by shrinkage or other factors that affect tissue geometry post-excision. Consequently, motivated by an increased interest in identifying enabling solutions for the development of fluorescence lifetime-based instrumentation suitable for clinical applications, this article focuses on reviewing the use of time-resolved fluorescence techniques (both point spectroscopy and lifetime imaging) for label-free characterization and diagnosis of tissue pathologies in human patients in vivo; and on indentifying challenges that hamper a broader translation of these techniques to clinical settings.

Overview of Time-Resolved Fluorescence Spectroscopy and Imaging Techniques

Fluorescence lifetime (time-resolved) measurements can be conducted in either the time-domain or frequency-domain. Both approaches provide equivalent information about the decay of fluorescence intensity. However, there are considerable differences in the implementation and applications of these two approaches. In the following we present a brief description of the principle behind the time-domain techniques primarily used for in vivo tissue investigations; specifically time-correlated single-photon counting (TCSPC) and pulse sampling and gated detection (Figs. 2a, 2b, 2c). For reference, we also describe the principle of the frequency-domain method (Fig. 2d).

Moreover, in practice fluorescence lifetime information from biological tissue is determined through either spectroscopy systems for point measurements (single channel) or imaging spectroscopy (multiple channels) systems. The latter also is known also as fluorescence lifetime imaging microscopy (FLIM). A comprehensive overview of point spectroscopy and FLIM techniques, both scanning and wide-field, and their potential applications have been extensively reviewed in previous publications.8,20,28,61

Figure 2
figure 2

Schematic of principles behind the main types of time-resolved (lifetime) measurement methods. (a) Conventional TCSPS; (b) Gated detection using a gated detector (e.g., gated ICCD camera); (c) Pulse sampling using a digitizer working at a high sampling rate and analog bandwidth; and (d) Frequency-domain (phase-modulation). Parameter M represents the demodulation factor or ratio. The ratios B/A and b/a represent the modulation of the emission and excitation, respectively. Schematics adapted from Lakowicz61

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Principles of Time-Resolved Techniques Used in Tissue Characterization

The time-resolved time-domain approach relies on instrumentation that measures the fluorescence emission as a function of time delay following pulsed excitation.20,22,28,61 TCSPC is the most commonly used technique for time-resolved measurements in both point spectroscopy and imaging systems.61 This technique has been not only extensively described in the literature8,20,28,61 but also implemented in general purposes commercial fluorescence lifetime systems such those developed by PicoQuant GmbH135 and Becker and Hickl GmbH.136 TCSPC has been generally preferred for fluorescence lifetime measurements due to its high sensitivity and low degree of systematic errors where the standard deviation of each measurement can be estimated from Poisson statistics. This is important in particular when the aim of the measurement is the resolution of complex intensity decay. In TCSPC the sample is excited by a pulse train of light and the emitted photons are recorded in a manner that at low emission fluxes a histogram of photon arrival time is build-up (Fig. 2a). This is achieved through recording a series of voltage signals that depend on the arrival time of individual detected photons relative to the excitation pulse. With a high repetition rate laser light source the fluorescence pulse response can be recorded in a short time. The resulting histogram counts vs. channels represent the change of fluorescence intensity vs. time. If a system for sample scanning is used, spatial information from the scanning electronics, a fluorescence lifetime images can be acquired. This represents a direct way to implement FLIM on conventional scanning microscopes.28,58,60 Overall, this technique has played an important role for the development of both point spectroscopy (TRFS) and imaging (FLIM) systems.

More recently, an alternative technique, pulse sampling and gated detection, has become popular in tissue diagnosis owing to recent advances in fast electronics and detectors. This technique can be implemented in two ways.61 In the first approach (Fig. 2b), a time-gated detector is used. In this case, a train of finite excitation pulses are used to induce the fluorescence emission of the sample. The fluorescence intensities at each distinct delay time along the decay curve are integrated by the detector and used to reconstruct the decay curve (intensity vs. time). Typically, a time-gated intensified charge-coupled device (ICCD) is employed for such measurement. This detection technique is preferred in wide-field FLIM.28,77 In the second approach (Fig. 2c), the detector is kept on until the entire fluorescence transient pulse is detected. If the sample quantum efficiency is high enough (above the detection system noise level) a single excitation pulse suffices in order to record a transient response pulse. The resulting electrical pulse signal is subsequently sampled using a digitizer (e.g., digital oscilloscope) with high sampling frequency and analog bandwidth (resolution of tens of picoseconds). The second detection method has been used by several groups for point spectroscopy TRFS12,13,72,79,85,92,94 and more recently scanning FLIM.111 Given its ability to rapidly collect fluorescence decays, this latter method is suitable for clinical applications. A constraint of this method, however, is the lack of information on the nature of the instrument noise. This may lead to the introduction of systematic error that could hamper an accurate recovery of the fluorescence decay. Overall, the TCSPC technique (single-photon) is characterized by high sensitivity and high temporal resolution but slow data acquisition. In contrast, the time-gated detection is characterized by fast and simultaneous acquisition of all pixels (i.e., FLIM), but lower sensitivity, and also requires a moderate sample fluorescence emission.

The time-domain technique requires complex opto-electronic equipment that challenges its clinical implementation. Typically, this technique involves fast wide-field (cameras) and point (photomultipliers) detectors for fluorescence detection and ps- and fs-pulsed lasers for fluorescence excitation. Also, there are limited options for pulsed lasers in UV range (330–400 nm) corresponding to the absorption spectra of the majority of endogenous fluorophores. The time-domain systems, however, present some unique advantages.20 For example the Fourier spectrum of a short laser pulse is broad enough to provide a wide band stimulus that allows complex fluorescence decay dynamics (multiple lifetimes) to be measured at once. In addition, pulsed laser systems working at low repetition rate (<10 kHz) do not require complete darkness and can be operated at room light, which make them compatible with clinical environment.20 Time-domain techniques based on both single- and multiphoton-excitation58,100 have been used for tissue fluorophores excitation. The latter approach carries specific advantages including the availability of high repetition rate and tunable pulsed lasers in near infrared (NIR) range. This type of lasers not only provides a broad range of tissue excitation wavelengths but also the use of NIR light for tissue excitation decreases the global tissue phototoxicity and improves the penetration depth. The technical complexity of multi-photon based instrumentation, however, has limited the use of this approach to sites that can be easily exposed to light excitation (e.g., skin). In fact, the only clinical diagnostic commercial system DermaInspect (JenLab GmbH)58,134 incorporating a time-resolved fluorescence measurement module is used for detection of dermatologic disorders.

The frequency-domain technique relies in an intensity-modulated excitation light source (Fig. 2d). This technique derives the lifetime information from measurements of phase difference (∅ω) between a sinusoidally modulated excitation and the resulting sinusoidally modulated fluorescence signal, where ω is the modulation frequency in radians/s. In addition, the finite response of the sample results in demodulation of the emission as defined by the demodulation factor M. 20,22,28,61 In tissue studies, the frequency-domain detection was initially preferred due to the relatively simpler electronic instrumentation and excitation source requirements. Nevertheless, measurements of complex intensity decay require repeated experiments at different excitation modulation frequency in order to take into account the entire fluorescence decay dynamics.20,61 This results in increased data acquisition and analysis time which has limited the suitability of this approach for clinical applications.

Time-Resolved Fluorescence—Data Analysis Methods

The fluorescence intensity decay profiles resulting from time-resolved measurements of tissues are often complex. Tissue endogenous fluorophore are characterized by a broad and overlapping absorption spectrum, thus typically more than one fluorophore are excited simultaneously. The measured fluorescence decay represents the superposition of all individual decay components that cannot be fit by a single-exponential decay model. In addition, the shape of the measured fluorescent pulse transient is affected by the optical and electronic components of the instrumental apparatus. Mathematically, the measured fluorescence intensity decay is the convolution of the fluorescence impulse response function (fIRF) with the instrument response. Thus, to estimate the fIRF of a sample, the instrument response must be deconvolved from the measured fluorescence pulse transient.61,88,126 For example in the context of discrete TRFS or FLIM data, the measured fluorescence intensity decay data y(n) is given by the convolution of the fIRF h(n) with the instrument response x(n):

$$ y(n) = T \cdot \sum\limits_{m = 0}^{K - 1} {h(m)x(n - m)} ,\quad n = 0, \ldots ,N - 1 $$

The parameter K determines the length of the fluorescence decay (fIRF), N is the number of time samples recorded for both y(n) and x(n), and T is the sampling interval (time-resolution of the instrument). Methods for recovery fIRF from tissue fluorescence measurements would ideally encompass a way to best estimate the shape of florescence decay and a rapid method for deconvolution. In the following we briefly overview the most common methods used in the analysis of fluorescence decay from time-resolved measurement performed in vivo in human tissues.

The most regularly applied deconvolution method is the least-square iterative reconvolution (LSIR) in conjunction with a multi-exponential approximation of decay function28,61,88,126:

$$ h(n) = \sum\limits_{i = 1}^{M} {A_{i} e^{{\left( {\frac{n}{{\tau_{i} }}} \right)}} } $$

where τi and A i represent the decay times and the corresponding amplitude coefficients, respectively. This technique applies nonlinear least-square optimization methods (i.e., Gauss–Newton, Lavenberg–Marquardt) to estimate the parameters of a multi-exponential fIRF that would fit best its convolution with the system response to the fluorescence decay data. Since the optimization process involves iterative convolutions, LSIR is typically computationally expensive. Also, since exponential functions do not form an orthonormal basis, distinct multi-exponential terms can be fitted to a particular fluorescence decay profile. This is due to the correlation of the fitting parameters in the multi-exponential model. Also, as noted above the fluorescence emission in tissues originates from several endogenous fluorescent sources. Thus the origin of autofluorescence in a complex medium such as biological tissues is not fully understood and often there is no a priori knowledge of the fluorescence decay model. The analysis of time-resolved fluorescence decay in terms of multi-exponential components therefore is not entirely adequate. Such components often cannot be interpreted in terms of fluorophore content or number of discrete lifetimes components.20,61 Thus there is an advantage in avoiding any a priori assumption about the functional form of the fIRF decay.

An alternative model-free deconvolution method for applied to fluorescence decay data analysis concerns expansion of the fIRF on a Laguerre basis of functions.50,51,68 This approach was found to provide significant advantages over the more traditional methods when applied to tissue characterization. The method is based on the expansion of kernels (fIRF, for linear systems) on a set of orthonormal set of discrete time Laguerre functions (LF) \( b_{j}^{\alpha } (n) \):

$$ h(n) = \sum\limits_{j = 0}^{L - 1} {c_{j} b_{j}^{\alpha } (n)} $$

In the equation above, \( c_{j}^{\alpha } \) are the unknown Laguerre expansion coefficients (LEC), which are estimated from the input–output data; \( b_{j}^{\alpha } (n) \) denotes the jth order ortho-normal discrete LF; L is the number of discrete LFs used to model the fIRF, thus defining the order of the expansion. The LF basis is defined as:

$$ b_{j}^{\alpha } (n) = \alpha^{(n - j)/2} (1 - \alpha )^{1/2}\sum\limits_{k = 0}^{j} {( - 1)^{k} \left( \begin{array}{l} n \\ k \end{array} \right)\left(\begin{array}{l} j \\ k \\ \end{array}\right)\alpha^{j - k} } (1 - \alpha )^{k} ,\quad n \ge 0 $$

The functions of higher order converge slower to zero. The LF include a build-in exponential term, thus they are suitable for physical systems with asymptotically exponential relaxation dynamics. The Laguerre parameter α (0 < α < 1) determines the rate of exponential (asymptotic) decline of the discrete LFs. The choice of the parameter α is important in achieving accurate fIRF estimations. In general, fIRF with longer lifetime would require larger values of α for efficient representation, and vice versa. In practice, the parameter α can be selected based on the length of h(n) and the number of LFs used for the expansion, so that all functions converge to zero by the end of the fIRF. Alternatively, these parameters could be also iteratively optimized. Overall, the Laguerre approach does not require a priori knowledge of the fluorescence decay function and allows for fast converging kernel estimation from short input–output data records, using least-square estimation of the expansion coefficients. Because the Laguerre basis is a complete orthonormal set of functions, it can always provide a unique and complete expansion of the fluorescence dynamics (decay function). Moreover, it enables fast deconvolution of input from the output as it resumes to solving a linear least-square minimization problem. Also, the Laguerre coefficients (c j ) can be used in the characterization of the fluorescence system. This method was adapted and successfully applied to the deconvolution of fluorescence decay data from tissues measured in vivo.12,13,68,72

Applications of Time-Resolved Fluorescence to Diagnosis of Pathologic Conditions in Humans

In the following we review the autofluorescence studies conducted in human patients in vivo. Emphasis is placed on the TRFS and FLIM techniques and their applications for (i) diagnosis of cancer of the GI tract, bronchi/lung, skin, head and neck, and brain; (ii) ophthalmic pathologies; and (iii) atherosclerotic cardiovascular disease.

Cancer Diagnostics

Gastrointestinal tract (GI) and bronchi: Conventional clinical endoscopic devices such as the white light endoscopes (WLE), bronchoscopes (WLB) or laryngoscopes (WLL) along with the availability of commercial endoscopic fluorescence imaging (e.g., Xillis LIFE, Stortz D-Light, Pentax 3000, or Richard Wolf DAFE)128–131 systems have enabled an extensive evaluation of autofluorescence as a tool for diagnosis of cancer in the GI tract (e.g., colonic polyps and Barrett’s esophagus) and cancer in the bronchi and larynx. However, as shown above (Table 1) the majority of these studies were based on measurements of fluorescence intensity in either imaging or spectroscopy format. Generally, the lower fluorescence intensity values along with specific changes in spectral emission profile exhibited by tumor when compared with the surrounding normal tissue were used for the delineation of the diseased tissue. The fluorescence measurement, however, in most cases was combined with conventional white light imaging and backscatter light to improve the diagnostic accuracy of Barrett’s esophagus127 or lesions in the bronchi.38,62 Only a limited number of studies39,85,92,122 employed TRFS-based analysis of these tumors. These are described in the following.

Similar to the steady-state techniques, the evaluation of the time-resolved techniques in patients in vivo took advantage of existing clinical endoscopic systems. A fiber-optic probe introduced in the accessory port of the endoscope was typically used to access the tissue of interest. One of the first TRFS reports85 concerns a pulse-sampling technique applied to the diagnosis of colonic polyps. The instrumental setup included fluorescence excitation via a pulsed nitrogen laser (337 nm, 4 ns, 20 Hz repetition rate) and detection via an avalanche photodiode and digital oscilloscope (500 MHz bandwidth, 2 GS/s sampling rate). This study targeted the discrimination of adenomatous and hyperplastic colonic polyps. Adenomatous polyps are a convenient model for dysplasia easily located and highly prevalent.92 The study was conducted in 17 patients (total 24 polyps; 13 adenomatous, 11 non-adenomatous). The results of this study indicated that the autofluorescence emission decay (excitation 337 nm, emission 550 ± 20 nm bandpass) from adenomas was faster than that from non-adenomas polyps (Table 1). No deconvolution method was used for analysis of the dynamics of the fluorescence decay rather the fluorescent pulse width was evaluated. While no specific dysplasia signatures were determined, the authors reported that the measured decay time provided a means of distinguishing adenomas from non-adenomas with a sensitivity (proportion of patients with disease who test positive) of 85%, and specificity (proportion of patients with disease who test positive) of 91%. The use of a much larger data set (237 polyps) in a subsequent report83 resulted in much lower levels of sensitivity and specificity (73 and 70%, respectively). Despite this successful application of TRFS to this site, few subsequent efforts have been made to extend the TRFS studies in this area.

Extending on the pulse-sampling TRFS instrumental setup described above, another study92 reported the potential of this technique for diagnosis of high grade dysplasia in Barrett’s esophagus (37 patients, 108 point measurements). The modified system allowed for the use of two excitation wavelengths (337 and 400 nm through the addition of a dye module to the nitrogen laser) and recording of fluorescence decay in 550 ± 20 wavelength range. Moreover, a spectrograph coupled to a diode array enabled simultaneous recording of the entire fluorescence emission spectrum, thus both wavelength- and time-resolved data were available for tissue characterization. The dynamics of the intensity decay was analyzed using a bi-exponential function. Although none of the parameters calculated from the time-resolved data permitted tissue classification with high accuracy, the individual decay trends for excitation at 337 nm demonstrate that the dysplastic tissue produces a faster decay than the non-dysplastic tissue (Table 1). This trend is similar to that obtained for the colonic polyps. However, no defined trends were obtained when tissue was excited at 400 nm. Overall in this study, the classification accuracy of spectrally resolved data alone was not improved by the addition of the time-resolved data.

A distinct TRFS instrumental setup for clinical tissue characterization during endoscopy was reported by Glanzmann et al.39 This instrument uses also a nitrogen laser pumped-dye laser (repetition rate between 1 and 50 Hz) to excite the tissue fluorescence. However, it employs a spectrograph to resolve the sample emission and a streak camera placed in the image plane of the spectrograph to spectrally (640 channels) and temporally (480 channels) resolve the fluorescence emission. This fast two-dimensional detector provided means to fast resolve the decay of the entire fluorescence spectra with a high dynamic range (subnanoseconds-to-miliseconds). An example of such measurement is given in Fig. 3. The initial validation of this system was demonstrated for nine case in vivo florescence measurement (case studies) in the bronchi, oral cavity, esophagus, and bladder.39 The measurements were conducted in patients undergoing routine endoscopy for cancer screening or photodynamic therapy. A more extended validation of this technique in humans was reported in a later study122 conducted in 11 lung cancer patients undergoing conventional WLB. Tissue autofluorescence was excited at 405 nm and the analysis of the fluorescence decay was based on multi-exponential decay function. Interestingly, the authors reported that although differences in the fluorescence (excited at 405 nm) decay parameters for distinct tissue conditions (preneoplastic, microinvasive neoplastic, and healthy mucosa in the bronchi) were found such differences are small in comparison to the inter- and intra-patient variations of the autofluorescence on healthy mucosa. The mucosa heterogeneities appeared to account for most of the changes of the lifetime(s) values. Despite the limited ability of lifetime data to finely discriminate distinct tissue pathologies, this study provided useful insights into the mechanisms underlying the autofluorescence spectral and intensity contrast in bronchial tissue.

Figure 3
figure 3

Example of a spectro-temporal fluorescence profile obtained using a streak camera placed in the plane of a spectrograph to spectrally and temporally resolve the fluorescence emission in multiple channels. Adapted from Glanzmann et al.118 Courtesy: G. Wagnieres, Swiss Federal Institute of Technology

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Oral Carcinoma

The potential role of autofluorescence contrast as a means of diagnosis of head and neck tumors including the cancer of the oral cavity was also extensively evaluated via a variety of point spectroscopy and imaging techniques.15,24,45,63,82,90,91,101,102,105,106,124 The relative direct accessibility of these tumor sites enabled the development of both point spectroscopy and wide-field imaging devices that can access the area of interest via flexible (fiber optic), semi-flexible (light guide), or rigid (direct illumination) light guides. The availability of the commercial device VELscope (LED Dental, Inc.)133 has also enabled clinicians to directly visualize fluorescence in oral cavity. However, as for the cancer in the GI tract or bronchi the majority of devices tested in patients were based on steady-state spectroscopy techniques or intensity-based imaging. In order to improve the specificity of the fluorescence measurements the clinical device incorporated complementary diagnostic techniques such as diffuse reflectance spectroscopy82,106 and light scattering spectroscopy,82 depth sensitive probes,105,106 advanced computational models of signal calibration, and digital imaging processing.101 Only two reports16,79 describe the use of TRFS point spectroscopy systems for characterization and diagnosis of oral carcinoma in patients. In both cases excitation and collection of the autofluorescence signal was achieved via customized handheld bifurcated fiber bundle probes.

The first study by Chen et al.16 employed a commercial available TCSPC system (HORIBA Jobin–Yvon IBH) consisting of a pulsed laser diode (excitation at 408 nm, pulse width 70 ps (FWHM)), a fast response photomultiplier, and a single photon counting device to study the autofluorescence emission (633 nm) corresponding to the porphyrin fluorescence as a means to differentiate in 38 patients normal oral mucosa from oral premalignant lesions including verrucous hyperplasia epithelial hyperplasia, and epithelial dysplasia. The fluorescence decay was fitted with a biexponential model. The research findings demonstrated that the fluorescence of normal mucosa at 633 nm emission exhibits a significantly faster decay dynamics when compared with dysplastic tissue (Table 1) that enables a good discrimination of distinct pathologies.

The second study conducted by our research group employed two fluorescence lifetime systems (TRFS and FLIM devices) for characterizing the fluorescence of cancer located on buccal mucosa, tongue, vocal cord and floor of the mouth. A subset of results from these studies was recently reported.79,116 Nine patients with suspected head and neck squamous cell carcinoma (HNSCC) were enrolled in intraoperative studies. TRFS data was recorded from 53 locations while FLIM images were acquired from 26 areas. For the TRFS we employed a pulse-sampling apparatus (Fig. 4) similar to that that reported earlier by our research group.30,113 This apparatus entails fluorescence excitation via a pulsed nitrogen laser (excitation at 337 nm, pulse width 700 ps (FWHM), 30 Hz repetition rate) and detection via a gated MCP-PMT and digital oscilloscope (2.5 GHz bandwidth, 40 GS/s sampling rate). A scanning monochromator allowed for sequential acquisition (360–610 nm spectral range, 5 nm interval) of fluorescence decay pulses along the entire sample fluorescence emission spectrum with a speed of ~0.8 wavelength decay profiles per second. Fluorescence decay was deconvolved using the Laguerre expansion technique. The averaged lifetime values were computed by interpolating the time the intensity decays to 1/e of its maximum value. Consequently, this apparatus enabled acquisition of both spectrally and temporally resolved fluorescence characteristics. Representative examples of point measurement data for normal vs. tumor are given in Figs. 5a and 5b.

Figure 4
figure 4

(a) Schematic of a versatile clinical system for time-resolved fluorescence studies in patients. Left panel: Setup for the TRFS mode; Right panel: Set-up for the FLIM mode. The fluorescence emission was induced via a 700 ps pulsed Nitrogen laser shared by the two setups. In the TRFS configuration the laser light was coupled into a multimode fiberoptic and delivered to tissue surface. Tissue fluorescence emission was collected by a ring of collection fibers, resolved by a monochromator, detected using a fast photomultiplier (PMT) tube, and resolved using a fast digitizer. In the FLIM configuration the excitation light was delivered via a single high numerical aperture fiberoptic and collected via a GRIN lens objective and a fiberoptic imaging bundle. Tissue fluorescence emission was spectrally resolved using a set of band-pass filters and detected and temporally resolved with a fast-gated ICCD camera. (b) Picture of a TRFS fiber-optic probe for collection of data from patients and picture of the distal end of this fiber probe. (c) Picture of a fiber-optic imaging bundle probe for acquisition of FLIM images from patients and picture of the distal end of this fiber bundle integrating the excitation and collection pathways. Details concerning these systems are described in Meier et al.79 and Sun et al.112

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Figure 5
figure 5

Upper panels. Representative examples of the TRFS emission of oral carcinoma located on vocal cord. (a) Normal vocal cord vs. (b) Tumor tissue. Lower panels: Representative examples of the FLIM images (4 mm field of view) of oral carcinoma located on buccal mucosa. (c) Normal mucosa vs. (d) Tumor tissue

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For the FLIM study we used a similar apparatus and procedure as previously described by our group for clinical investigation of brain tumors in patients112 and pre-clinical investigation of oral carcinoma in a hamster animal model.114 The FLIM setup was based on wide-field time-domain imaging technique using a gated optical intensified CCD camera with a minimum gating time of 200 ps to resolve the fluorescence intensity decay. The fluorescence was excited and collected via a customized imaging probe including an excitation fiber with high numerical aperture (NA = 0.48) and a collection fiber image bundle (10,000 fibers, ~0.6 mm total diameter) and a GRIN lens objective (FOV = 4 mm). The pulsed nitrogen laser used in the TRFS system described above was also used as excitation light source in the FLIM system. The fluorescence emission was collected via a band-pass filter 460 ± 25 nm. Fluorescence collected within this spectral band is primarily correlated to NAD(P)H fluorescence. Both the TRFS and the FLIM system were placed on the same cart and when clinically feasible enabled collection of data from the same patient(s) with both systems. The fluorescence decays were deconvolved using the Laguerre expansion technique.50,51 Representative examples of FLIM measurement data for normal vs. tumor are depicted in Figs. 5c and 5d. The two independent studies (TRFS79 and FLIM,116 respectively) demonstrated that upon 337 nm excitation features derived from the dynamics of the fluorescence decay in 460 ± 25 nm range allows for the discrimination of oral carcinoma tumors (regardless their location) from normal tissue. Overall, the tumor exhibited a faster decay dynamics when compare with normal tissue. Such differences are most probably attributed to different ratios of free and bounded NAD(P)H in the two tissue types. When bound to proteins, the NAD(P)H fluorescence lifetime is known to increase multifold.17,61 The fluorescence intensity also play an important role in distinguishing tumor vs. normal within this spectral emission range with a lower intensity in tumor tissue vs. normal tissue, trends in agreement to steady-state fluorescence intensity studies reported by other groups.15,45,82,90,91,101,102,106,124

Primary Brain Tumors

Intra-operative brain tumor diagnosis and delineation of surgical margins represents another important clinical target for autofluorescence technique. In contrast to other types of tumor primary brain tumors are infiltrative in nature and often do not have a well defined border. Techniques for intraoperative diagnostics are important in assisting the neurosurgeons in delineating tumor cells infiltration in normal brain tissue and to evaluate the presence of positive margins. A few groups19,65,66,120 investigated the use of steady-state fluorescence spectroscopy (SSFS) technique complemented by diffuse reflectance spectroscopy as an intraoperative diagnostic tool. Such tool can potentially aid in either the surgical resection of gliomas or in the evaluation of radiation damage in brain tissue. A high sensitivity (94%) and specificity (93%) were reported for the delineation of normal vs. infiltrating tumor margins.120 Paradoxically, in the same study lower sensitivity (80%) and specificity (89%) values were founded for delineating normal vs. tumor. Although promising for real-time analysis during the course of resection, no subsequent studies involving this technique have been reported.

Using the TRFS and FLIM techniques described above for oral carcinoma, our group also investigated the emission of primarily brain tumors.12,13,112 The fluorescence lifetime characteristics of both high and low grade glioma were evaluated and contrasted against those obtained for normal cortex and normal white matter. Studies were conducted in patients undergoing surgery for brain tumor removal via customized fiber-optic probes that can be used to remotely access the brain tissues during conventional craniotomy procedure. The TRFS studies (337 nm excitation, 360–500 nm emission range) were reported in two publications for 17 patients12 and 42 patients,13 respectively. In contrast to most other cancer types, brain tumor autofluorescence is primarily associated with the emission of metabolic co-factors such as NAD(P)H and FAD. Typically, no structural proteins are present in glioma tumors unless the tumor undergoes radiation therapy and necrosis that may result in formation of collagen fibers. While scarcely reported,121,140 pyridoxine and glutamate decarboxylase can also contribute to the brain tissue autofluorescence with 370–400 nm wavelength range. Results from these TRFS studies demonstrated that upon 337 nm excitation both the relative intensity ratio between 390 ± 10 and 460 ± 10 nm emission bands as well as the dynamics of the fluorescence decay (averaged lifetime and Laguerre coefficients) within this bands play an important role in the discrimination normal brain tissues from both low and high grade gliomas. Representative results from these studies are depicted in Fig. 6.12 Moreover, using parameters from both spectral and time domain we were able to classify low-grade gliomas with very high sensitivity and specificity (100 and 98%, respectively).13 However, for high-grade gliomas the sensitivity was low (47%), while the specificity was high (94%). The low sensitivity was attributed to the heterogeneous nature of these tumors as well as phenotypic diversity. The FLIM study112 conducted in three patients (13 sites) were in agreement with the TRFS results suggesting that the fluorescence decay dynamics at about 460 nm emission plays an important role in glioma discrimination. While these overall findings are promising, more systematic studies are needed in order to account for gliomas genetic sub-classification associated with prognostics. To our knowledge the fluorescence emission of the diverse brain tumor phenotype (e.g., malignant astrocytomas, mixed gliomas and oligodendroglioma) has not been fully investigated in order to be able to conclude on the ability of fluorescence signatures to analyze and distinguish gliomas.

Figure 6
figure 6

Representative examples of TRFS parameters that enable differentiation of distinct tissue types. (a) Intensity ratio 390 vs. 460; (b) average lifetime at 390 nm; (c) Laguerre coefficients at 390 nm. (NC = Normal Cortex; NWM = Normal white Matter; HGG = High Grade Glioma; LGG = Low Grade Glioma). On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles. Note: Low grade glioma demonstrates low variance as compared with high grade glioma and can be easily distinguish from normal brain tissue. Adapted from Butte et al.12

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Other Cancers

Detection of cervical dysplasia and cancer represents one of the most successful applications of SSFS technique in combination with reflectance spectroscopy. Pertinent reviews were recently published.14,118 Several research groups33,48,87,98 conducted research in this area and more recently commercial devices based on this approach have been developed. For example, LUMA™ Cervical Imaging System manufactured by MediSpectra, Inc. (Lexington, MA) received premarket approval from the FDA in 2006. To evaluate cervical abnormalities this system uses autofluorescence, diffuse reflectance backscatter, and video imaging. The FDA indicated its use as an adjunct tool to colposcopy for identification of high-grade disease in women referred for colposcopy. Another example is the Guided Therapeutics’ LuViva Advanced Cervical Scan132 currently seeking premarket approval for the early detection of cervical pre-cancer. This system integrates also autofluorescence and DRS to analyze cervical neoplasia. Interestingly, the optical spectroscopy has been found to have similar performance with colposcopy and suggested as an effective adjunct to colposcopy.14,118 Although the overall specificity of optical spectroscopy is lower when compared with standard clinical tests, their relative high sensitivity appears to offset the lower sensitivity of the conventional tests. However, no TRFS or FLIM studies in patients were conducted in this area. The inherent nature of the fluorescence lifetime measurement can potentially improve the specificity of the fluorescence measurement conducted in cervix and consequently this can be a new area of application for time-resolved techniques. Another SSFS area of application has been the diagnosis of breast tumors. A few recent articles53,139 reported the use of this technique as a tool to image-guide core needle biopsy of the breast (73 patients) and imaging of breast tumor surgical margins (28 patients). To our knowledge, no fluorescence lifetime study was conducted in patients diagnosed with breast tumor.

Applications of Time-Resolved Fluorescence to Human Skin

Several fluorescence techniques have been employed for the characterization of skin autofluorescence in order to evaluate their ability for analysis of skin physiology, optical biopsy of skin, and detection of dermatological disorders including fungal infections, skin age, hair pigment, and cancer. Typically, the fluorescence emission of endogenous bio-molecules such as flavins, NAD(P)H coenzymes, metal-free porphyrins, components of lipofuscin, melanin, elastin, collagen and keratin is exploited as a contrast mechanism for skin characterization. The direct accessibility of skin has enabled the use of a broad range of fluorescence-based devices including multi-photon excitation and high-resolution scanning system as well as delivery and collection of light via rigid and free-optics. This alleviated the need for remote access of tissue via flexible fiber-optics that typically results in lower fluorescence light excitation and collection efficiency, and allows for an easier implementation of tissue scanning systems. Devices based on both steady-state fluorescence11,26,44,55–57,59,73,89,96 and time-resolved fluorescence techniques were reported.10,25,58,74,94,100 Several studies demonstrated that SSFS techniques show potential for discrimination of non-melanoma skin cancer.10,94 However, the accuracy of discrimination depends strongly on the skin type of the patient89 and the specificity remains low even when DRS is used to correct the fluorescence spectra.96

Notably, multi-photon fluorescence excitation scanning techniques including FLIM have been suggested as most appropriate for skin analysis as such approaches offer the opportunity for non-invasive optical sectioning (high spatial resolution and penetration depth) of skin and disease stage in vivo. Comprehensive reviews of fluorescence lifetime studies of human skin were recently reported by Koning et al.59 and Roberts et al.100 Over a decade ago Masters et al.74 demonstrated the first use of a multi-photon scanning FLIM system to image in vivo human skin. Subsequently, several other groups reported the use of FLIM techniques for human skin analysis post-excision as summarized by Roberts et al.100 While these ex vivo studies provided numerous insights in the ability of fluorescence lifetime techniques to characterize distinct features in skin related to either pathologic or non-pathologic conditions, measurements of excised skin being stored or frozen are not fully suitable for establishing the diagnosis accuracy of this technique. For example, freezing is known to lead to loss of NAD(P)H, the dominant fluorophore in the viable epidermis, thus likely to affect the fluorescence emission features of skin measured ex vivo. The development of a commercial clinical multiphoton tomography system, DermaInspect (JenLab GmbH), enabled a series of skin studies in vivo in humans.134 This system includes a FLIM module consisting of a tunable NIR light source (titanium:sapphire laser: repetition rate of 80/90 MHz, pulse width 75–120 fs FWHM), a x-y galvano-scanner module, and a TCSPS unit. This technique is not only compatible with fast scanning but also capable of recording FLIM images in several wavelength channels simultaneously. Typically, 128 time channels covering 50 ps per channel were used for FLIM imaging. A multi-exponential fit was typically used for the analysis of fluorescence decay. A variety of dermatologic disorders (e.g., skin allergies, psoriasis, trichophytosis, and cancer) have been imaged with this system. Studies were conducted in more than 200 patients (Caucasian skin). For example, Dimitrow et al.26 report a clinical study in 83 melanocytic skin lesions. Six distinctive features of malignant melanoma were specified and statistically evaluated in this study. Their results demonstrated distinct morphological differences in melanoma compared with melanocytic nevi and relatively high sensitivity values up to 95% (range: 71–95%) and specificity values up to 97% (range: 69–97%) were reported for diagnostic classification. The architectural disarray of the epidermis, poorly defined keratinocyte cell borders as well as the presence of pleomorphic or dendritic cells were found to play an important role for discrimination of distinctive features of melanoma and to improve the classification accuracy. An earlier report from the same group25 also showed that the fluorescence emission peak at about 560 nm may enable the discrimination of melanomas from naevae.

A few TRFS systems were also tested in humans and reported,74,94 however such reports are sparse. For example, Blackwell et al.74 demonstrated the use of a TCSPC based technique to study the characteristics of time-resolved autofluorescence of skin as a function of location, gender, skin complexion and age. The system employed a pulse LED (375 nm excitation, 700 ps FWHM) as excitation source and a photomultiplier coupled to a monochromator. The time decay emission was recorded at four different emission wavelengths (442, 460, 478 and 496 nm). Data was fitted using a multi-exponential model. This study suggested that specific decay components can be related to free and bound NADH while others are related advanced glycation end product (AGE) crosslinks. However, to our knowledge subsequent efforts to extend the TRFS studies in this area have not been made.

Applications of Time-Resolved Fluorescence to Eye Diseases

A distinctive application of FLIM concerns the measurements of human eye for detection of metabolic alterations in early stages of diseases such as age-related macular degeneration and diabetic retinopathy, when no changes are visible at the eye ground. Studies in this area were primarily reported by Schweitzer research group.42,107–109 Time-resolved fluorescence measurements of the eye are in particular challenging due several reasons. This includes limitation concerning the use of laser light for exciting the eye fluorescence (wavelengths and permissible exposure according to the ANSI Standard limits), the efficiency of light transmission through the ocular media, and the ability to define a single image plane as the eye anatomy and its multilayer structure (cornea, lens, fundus with layers of nerve fibers and ganglion cells, etc.) varies with individuals. Also, the eye is an object that moves randomly. Thus, in contrast to measurements at the microscopic scale of a stationary sample, the eye movements must be compensated by online image registration. Schweitzer et al.109 reported a FLIM-based laser scanner ophthalmoscope able to address such challenges. Figure 7 depicts a schematic of a clinical ophthalmoscope developed by this group and an example of data collection and online display of this instrument. The fluorescence is excited by picosecond laser (448 nm excitation, 70 ps FWHM, 80 MHz) in a 30° eye fundus field. The mean power of excitation laser light used for excitation was about 100 μW in the cornea plane. The fluorescence was detected in two spectral ranges (490–560 and 560–700 nm) by TCSPC module. The time resolution of the system was 12.2 ps. The fluorescence decays were optimally fitted by a 3-exponential model. In the fitting process the decay model was convolved with the experimentally determined instrument response function. Current studies have demonstrated that a good correspondence can be found between the first component (most frequent lifetime about 60–90 ps, amplitude about 85%) and the retinal pigment epithelium in the short wavelength channel. The second component (lifetime about 450 ps, amplitude about 12%) was found to correspond to the neuronal retina. The third component (lifetime about 3 ns, amplitude about 3%) originated from connective tissue in the choroid and was covered by the fluorescence of the crystalline lens. The local alterations can be demonstrated in images of fluorescence lifetimes, amplitudes, or relative contribution as depicted in Fig. 7. The global changes can be estimated from statistical comparison of histograms of FLIM parameters. Missing or additional appearing fluorophores can be calculated from the difference of lifetime histograms in regions of interest of patients and healthy subjects. However, the interpretation of FLIM results was found not trivial. The contribution of multiple fluorophores situated in different layers of the fundus of the eye to the overall fluorescence intensity decay cannot be fully described via the multiexponential model used in the study. Nevertheless, these studies demonstrate a very unique and challenging application of FLIM.

Figure 7
figure 7

Upper panel: Schematic diagram of the scanning FLIM ophthalmoscope. The fundus is excited by ps-pulse lasers at 448 or 468 nm. The fluorescence is detected in 2 spectral intervals ch 1: 490–560 nm and ch 2: 560–700 nm using TCSPC. In addition to the excitation laser, the fundus is illuminated by a cw IR laser for online image registration. Abbreviations: Multichannel plate photomultiplier (MCP-PMT); dichroic mirror (DM); band-pass filter (BF). Lower panel: Example of FLIM data collection (screenshot) in the short wavelengths channel. In this particular case the images indicate the relative contribution of neuronal retina fluorescence (Q2—left image) and connective tissue fluorescence (Q3—right image) of a patient suffering from advanced age-related macular degeneration. The relative contribution (Q2) in the neuronal retina is expected to be reduced in the damaged macula, while the relative contribution of the connective tissue (Q3) is considerably increased in the damaged macula. The field corresponds to 150 × 150 pixels (40 × 40 μm2 pixel size). The histograms in the bottom line are used for statistical evaluation. Courtesy of D. Schweitzer, University of Jena, Germany

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Applications of Time-Resolved Fluorescence for Research and Diagnosis of Atherosclerotic Cardiovascular Diseases

The composition of atherosclerotic plaque plays an important role on plaque instability and rupture.47,64,67,80,86 Such event typically results in either heart attack or stroke depending on the plaque location.64,86 Given the ability of fluorescence techniques to detect elastin, collagen, lipid components and other sources of autofluorescence in normal and diseased arterial wall these techniques were extensively explored as a means of research and diagnosis of atherosclerotic cardiovascular diseases. Importantly, most recent studies have shown the application of fluorescence techniques to the identification of plaque disruption,18 detection of plaques with thin fibrous cap,3 and discrimination of lipid-rich lesions.69 Such features are a hallmark of plaque vulnerability. Both steady-state methods and time-resolved methods to characterize the biochemical composition of atherosclerotic plaques were reported.2,3,18,29,47,67–69,80,93,104 A review of fluorescence lifetime techniques to atherosclerotic plaques was recently published by Marcu.68

Despite TRFS and FLIM potential for analysis of atherosclerotic diseases, most studies based on these techniques were conducted primarily ex vivo in excised samples or in vivo in atherosclerotic animal models.2,3,6,7,18,47,69,70,80,81 Only a few recent publications present applications of a point spectroscopy TRFS device72 and a multimodal (reflectance, fluorescence, and Raman) optical spectroscopy technique104 for characterization of plaque fluorescence in vivo in patients. Both studies took advantage of the carotid endarterectomy (CEA) procedure. Carotid plaque represents an excellent study model for the validation of optical devices for detection of plaque composition in patients. The conventional clinical procedure allows for direct access of plaque in vivo during surgery using fiber-optic probes (with or without distal rigidity) and without the need of intravascular procedures. Also, the routine removal of the plaque during CEAs permits extensive optical studies ex vivo in fresh plaque specimens that account for the plaque composition heterogeneity and facilitates a direct validation of optical results against histopathological analysis. However, even in such CEA studies the total number of patients enrolled was limited. This is due to either the time available to conduct optical measurements without affecting the course of surgical procedures or the ability to mark and subsequently conduct histopathologic analysis of the exact area that underwent optical investigation. For example, our group employed a pulse-sampling based technique TRFS apparatus,30 similar with that described above for analysis of oral and brain tumors, to study the fluorescence emission of carotid plaques and to determine the ability of this technique to delineate markers of plaque vulnerability.72 This study was conducted in a total of 65 patients (813 locations) scheduled for CEA, but clinical conditions allowed for only 14 patients (28 locations) to be investigated during the CEA intervention. Moreover, out of the 28 point measurements in vivo only ten of them could be correlated with the histopathologic analysis of plaque. This small final number underscores the challenge validating the fluorescence results against gold standard methods in histopathology.

While such intraoperative investigations provided insights into the ability of fluorescence measurement to analyze plaque composition in vivo and to analyze difference between in vivo and ex vivo measurements, this approach for data collection is impractical in actual clinical situations. Practical application would require development of flexible catheters suitable for interrogation of the vessel wall during catheterization procedures. To our knowledge no such study has been conducted intravascularly. This is primarily due to intrinsic challenges posed by optical measurements in the presence of blood flow and in small and tortuosity of the arteries that would require advanced engineering of specialized catheters and blood flushing systems. A constraining factor is also the limited ability of most TRFS and FLIM systems to rapidly record time-resolved fluorescence data. Validation of these systems in patients would require recording of data from numerous points or from relatively large areas in order to account for atherosclerotic plaque heterogeneity and therefore to assess their diagnostic abilities. Most recent efforts attempt to address such challenges through the development of a scanning TRFS device111,115 that can record fluorescence decays in multiple spectral bands in a few microseconds. This device was already tested in vivo in a pig artery115 under intravascular ultrasound (IVUS) guidance (Fig. 8). A site viewing fiber optic and a low flow blood flushing system enabled continuous recording of fluorescence data from the arterial wall. This work demonstrated for the first time the ability of a TRFS system to collect robust in a blood vessel in vivo under continuous pull-back motion. Current efforts are directed towards adapting such TRFS system for recording data during radial scanning as well as pull-back motion in a similar manner as the IVUS technology. Such approach will allow a direct integration of fluorescence lifetime techniques with conventional intravascular procedure.

Figure 8
figure 8

The fluorescence spectral parameters from scanning TRFS during a pull-back sequence in the right common iliac of a swine. The distance between the artery wall and catheter over the pull-back is shown at the top left in (a). The fluorescence intensity and lifetime for the three spectral bands are shown in (b) and (c) respectively. The IVUS image frames at the right show the estimated distance of the fiber optic from the arterial wall at two moments in the catheter pullback procedure. Note that while the fluorescence intensity (b) varies significantly over the scanning period, the lifetime values (c) remain relatively constant

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Challenges in Clinical Translation for Time-Resolved Fluorescence Techniques

Most peer-reviewed articles reporting applications of label-free fluorescence techniques for clinical diagnosis of tissues are summarized in Table 1. Close inspection of these reports clearly demonstrates that, overall, steady-state measurements (both point spectroscopy and imaging) provide a very good sensitivity but low specificity for identifying specific conditions in tissue. Complementary techniques such as diffuse reflectance spectroscopy enabled improvement of fluorescence measurement specificity. For example, commercial devices based on SSFS and DRS employed in clinical trials have demonstrated that a combination of these optical signatures is very sensitive to malignant transformations, in particular detection of malignant transformations of the GI tract, bronchi, and cervix. However, even in a multimodal configuration, the overall specificity remains relatively low when compared with conventional histopathological methods.

Inherently the time-resolved measurement of fluorescence can improve the specificity of fluorescence measurements. Consequently, time-resolved techniques can play an important role in improving the overall diagnostic accuracy in tissue. Given the limited number of studies employing time-resolved techniques as well as the limited number of patients enrolled in these studies however, the diagnostic potential of this technique cannot be fully evaluated. In fact, the total number of reports involving time-resolved measurements of fluorescence for all investigated sites (e.g., GI tract, respiratory tract, oral cavity, brain, skin, eye, and arteries) is less than 25. Moreover, except for the applications of FLIM techniques to skin disease diagnosis (primarily using the commercial system DermaInspect), all studies targeting other anatomical sites have enrolled a small number of patients; typically less than 20 patients. This small number of both clinical research studies and patients enrolled clearly demonstrate that the potential of fluorescence lifetime technique for clinical diagnostics is far from being fully explored.

In general, the critical aspects that have hampered a more complete evaluation of time-resolved fluorescence techniques in patients include: (1) the rather complex and expensive instrumentation and limited availability of light sources and sensitive detectors required by time-resolved fluorescence techniques when compared with intensity/spectral techniques. This in turn limits the development of practical clinical research instrumentation; (2) the slow data acquisition speed that characterizes the majority of TRFS and FLIM devices. This limits the number of data points or images recorded from patients, thus impeding the statistic evaluation of the results; and (3) the lack of analytical methods for accurate representation of fluorescence decay profile from measurements in tissues and difficulties in the interpretation of this profile in relationship to tissue pathophysiology.

Availability and Affordability of Instrumentation for Practical Time-Resolved Measurements

The majority of the clinical studies involving TRFS or FLIM techniques described above, except for FLIM applications to skin analysis, have employed pulsed nitrogen lasers as the light source for fluorescence excitation. While these UV lasers have been reliable for most studies, their low repetition rate (<50 Hz) has precluded the development of high-speed data/image acquisition systems. Ultraviolet and blue range emission picosecond (as short as 50 ps) pulsed diode lasers (e.g., PicoQuant135), operating at high repetition rate (up to 40 MHz) represent a good alternative. However, the peak power output of these lasers is low in the UV range where most biological fluorophores are efficiently exited. This also results in long data acquisition times due to the need for averaging in order to improve the fluorescence signal-to-noise ratio. New picosecond pulsed solid-state lasers including compact fiber lasers are currently commercially available and could play an important role in future applications.

Mode-locked tunable Ti:Sapphire lasers have been the workhorse for multiphoton FLIM,58 but their very high pulse repetition rate (typically at about 80–90 MHz) is not suitable for many biological fluorophores. While the repetition rates of these lasers can be reduced using appropriate pulse pickers, the fs-pulsed tunable laser systems for 2p-excitation are expensive, sometimes technically complex, and cumbersome to operate in clinical settings. Moreover, in TRFS and FLIM applications the characteristics of the excitation source are often intertwined with those of the detector.

Limited availability of fast and sensitive detectors represents another challenge for clinical applications of these techniques. Very few detectors have been used in clinical studies. These include fast MCP-PMTs or avalanche photodiodes, streak cameras (640 spectral × 480 temporal channels), gated ICCD cameras (repetition rate maximum of 10 MHz, time gate 200 ps), and TCSPS detection modules involving multiple detection channels/PMTs. Clinical FLIM systems, in particular, will benefit from the availability of fast gated, sensitive cameras that allow for wide-field imaging at high repetition rate.

While scanning methods can be relatively easily implemented in applications that enable direct access to tissue or organ of interest (e.g., skin or eye), this may not be trivial to implement in endoscopic applications or during surgery. A promising wide-field FLIM system based on a gated optical intensifier (GOI) coupled to a CCD camera was recently reported.78 In this system, images of the exponentially decreasing fluorescence decay were recorded at 25 different time points at 250 ps intervals. A typical acquisition takes ~4 s. For each sample, five decays were acquired sequentially and averaged. Thus, the total acquisition time of this system for acquiring a FLIM image from tissue biopsy specimens was ~20 s.

Overall, a broader use of TRFS and FLIM techniques in clinical settings relies on the availability of affordable picosecond pulsed lasers and fast and sensitive detector technologies. Technical solutions that enable the use of multiple wavelengths (tunable light sources) for tissue excitation as well as resolve the fluorescence decays at multiple wavelengths simultaneously (hyperspectral TRFS or FLIM detection)23,115 can also play an important role in the evaluation of lifetime fluorescence techniques potential for clinical diagnosis of diseases.

Analysis of Fluorescence Decay and Interpretation of the Decay Profile

Another challenge is posed by the complexity of fluorescence decay profiles derived from tissue measurements and our ability to accurately represent and interpret the rich information content within this decay profile. Not only is the tissue autofluorescence generated by several biological molecules with overlapping spectral emission, but also the fluorescence emission characteristics of these biological molecules depends on their physiochemical environment. In addition, the effects of tissue architecture may affect the volumetric/depth distribution of the fluorophores. Consequently, for tissue autofluorescence, there is no a priori knowledge of the intensity decay model.

The majority of fluorescence lifetime studies conducted in patients used an exponential approximation (typically bi-exponential) to evaluate the fluorescence decay dynamics. While this approach may provide some insight into the type and number of the dominant fluorescent molecules in tissue, this representation most likely cannot capture the number of actual multi-exponential components in the decay profile. Ultimately, the bi-exponential approximation of complex biological tissue fluorescence is just a convenient way to analyze and display the TRFS and FLIM data, but this representation may not capture or use the entire information content within the intensity decay profile.

The use of Laguerre functions as a means of representing the fluorescence decay characteristics has some intrinsic advantages as described above. This deconvolution technique has primarily been used by our research group. We demonstrated that this method provides a model-free representation of decay dynamics as well as a much faster approach to fIRF deconvolution. Subsequently it supports the further development of real-time (on-line) TRFS or FLIM diagnostic systems.

The ability to accurately retrieve and parameterize the intensity decay profile is only a step towards resolving the fluorescence decay from biological tissues. A subsequent challenge emerges from the limited ability to interpret the numerous fluorescence parameters as a function of tissue patho-physiological conditions. Validation of fluorescence data acquired in vivo is based on conventional histopathological analysis conducted on tissue specimens/biopsy removed from optically interrogated tissue regions. This is subject to both sampling error as well as changes in tissue conditions post-excision (physiology, metabolism, morphology, etc.). Consequently a quantitative interpretation of the fluorescence decay data is virtually impossible, in particular for metabolically active tissues such as tumors. New paradigms for analysis fluorescence decay and interpretation of time-resolved decay parameters as a function of tissue pathophysiology are needed in order to evaluate the full diagnostic potential of time-resolved techniques.

In addition, problems with the interpretation of decay profiles may arise when comparing information generated with different instruments. The instrument response function as well as the computational method for fluorescence decay profile analysis is likely to affect the numerical values of various parameters used for characterization of fluorescence decay (e.g., lifetime(s), decay constants, Laguerre coefficients, etc.). A “universal standard” for calibration of different time-resolved fluorescence instrumentation could play an important role in analytical chemistry/biochemistry and biophysics, but such standards may still be challenged when the instrumentation is used in tissues analysis.

Given the biological complexity of tissues and subsequently the complexity of the fluorescence decays measured from biological tissue, in our view the development of a clinical instrument would rather benefit from calibration procedures that enable systematic identification of certain conditions in tissue, rather than accurate recovery of specific decay parameters. While absolute decay parameter values can be used for a quantitative interpretation of fluorescence data and are essential for research in biochemistry and biophysics, this may not necessarily fully address the needs for clinical tissue diagnosis. Perhaps, a method for robust qualitative analysis of fluorescence data capable of providing a fast and accurate recognition/staging of specific pathologies in tissues would better serve the immediate clinical needs.

Limited Tissue Penetration Depth

An inherent challenge for the fluorescence techniques is the shallow penetration depth of the excitation light. Single-photon excitation (ultraviolet range) provides a tissue interrogation depth below 0.5 mm, while multi-photon excitation (near-infrared range) enables higher penetration but still below 1.5–2 mm. Consequently, this confines the applicability of TRFS and FLIM to tissue/organ surfaces. However, these techniques are very sensitive to early pathological molecular changes that typically occur at the tissue/organ surface and are even capable of spatially resolving such changes with high resolution (e.g., multi-photon based systems). Generally, conventional imaging or spectroscopic techniques with higher penetration depth cannot sense and resolve early pathological transformations. Also, most conventional imaging techniques (e.g., optical coherence tomography) provide structural rather than biochemical and functional information. Perhaps, future development of hybrid systems combining techniques capable of providing complementary information and of interrogating tissues at variable depths with “pathologic” resolution will expand the diagnostic ability of current fluorescence-based techniques.

Conclusion

Clinically compatible time-resolved fluorescence spectroscopy and imaging systems developed by several research groups have demonstrated that fluorescence lifetime provides a means of achieving optical molecular contrast in diseased tissues measured in patients. Also, they have shown that fluorescence lifetime contrast can be used in tissue characterization and diagnosis. However, as reported in literature, the total number of studies as well as patients involved in TRFS or FLIM clinical research is very small. Thus, no definitive conclusion can be drawn regarding the potential of these techniques for medical diagnosis. Systematic studies of the fluorescence decay characteristics in patients along with evaluation of intra-patient data variability are critical for a complete assessment of fluorescence lifetime techniques ability for accurate clinical diagnosis.

Current efforts toward identifying new technical solutions for fast recording of spectrally resolved fluorescence lifetime information, combined with advancement of new analytical methods for fast processing of the fluorescence intensity decay data, are enablers for further development of compact diagnostic TRFS and FLIM systems. Such solutions can initially facilitate further development of practical devices for clinical research studies and methodical evaluation of their diagnostic potential. Extrapolating on the current clinical successes of the fluorescence systems based on intensity and spectral information only, it is anticipated that the addition of time-resolved information will improve the accuracy for diagnostics of these devices.

Since several SSFS devices, including commercial systems, have been extensively evaluated in patients and involved in FDA approved clinical trials, a potential approach to rapid evaluation of TRFS and FLIM devices in the clinical environment is adding a simple time-resolved detection module to existing SSFS instrumentation platforms. This can provide a direct venue to systematically verify whether and how lifetime information can be used to improve the specificity of fluorescence measurements. A successful outcome will alleviate the need for coupling SSFS devices with other spectroscopic techniques (e.g., diffuse reflectance spectroscopy) or other cumbersome intensity calibration and correction procedures. Advancement of simple solutions for clinical implementation of TRFS and FLIM devices has the potential to impact the clinical management of disease by enabling early diagnosis, real-time detection of surgical margins, and monitoring the effect of therapeutic interventions.