Gold nanoshells (dielectric silica core/gold shell) are a novel class of hybrid metal nanoparticles whose unique optical properties have spawned new applications including more sensitive molecular assays and cancer therapy. We report a new photo-physical property of nanoshells (NS) whereby these particles glow brightly when excited by near-infrared light. We characterized the luminescence brightness of NS, comparing to that of gold nanorods (NR) and fluorescent beads (FB). We find that NS are as bright as NR and 140 times brighter than FB. To demonstrate the potential application of this bright two-photon-induced photoluminescence (TPIP) signal for biological imaging, we imaged the 3D distribution of gold nanoshells targeted to murine tumors.

We present a lookup table (LUT)–based inverse model for determining the optical properties of turbid media from steady-state diffuse reflectance spectra that is valid for fiber-based probe geometries with close source-detector separations and tissue with low albedo. The lookup table is based solely on experimental measurements of calibration standards. We used tissue-simulating phantoms to validate the accuracy of the LUT inverse model. Our results show excellent agreement between the expected and extracted values of the optical parameters. In addition, the LUT represents a significant improvement in accuracy at short source-detector separations (300 μm) and low albedo (~0.35). We also present in vivo data from clinically normal and malignant nonmelanoma skin cancers fit to the LUT-based model. Diffuse optical spectroscopy (DOS) has widely been used to noninvasively characterize tissue optical properties for disease diagnosis. DOS uses optical fiber probes to measure broadband diffuse reflectance spectra from the tissue surface. Collected reflectance spectra are analyzed to extract optical scattering and absorption properties that indicate tissue pathology (i.e., tissue microarchitecture and function).^{1, 2} Many current strategies for analyzing diffuse reflectance rely on the solution to the diffusion approximation of the radiative transport equation,³ or a modified form.² However, the diffusion approximation is not valid at source-detector separations less than approximately one reduced mean free path $[1∕({\mu }_{\mathrm{s}}^{\prime }+{\mu }_{\mathrm{a}})]$ and in tissues with low albedo $[{\mu }_{\mathrm{s}}^{\prime }∕({\mu }_{\mathrm{s}}^{\prime }+{\mu }_{\mathrm{a}})<0.9]$ . In addition, many inverse solutions employing the diffusion approximation are computationally intensive. Because most cancers originate in the epithelial layer at the tissue surface, DOS systems that sample spectra from the tissue surface are highly desirable.⁴ Also, an important indicator of early cancer, angiogenesis, can lead to significantly higher absorption due to blood at a level comparable to scattering. Unfortunately, the diffusion-approximation-based inverse models are not accurate in many of these regimes. To overcome this limitation, several recent models based on Monte Carlo⁵ or higher-order approximations⁶ to radiative transport have been developed. In addition, researchers have also developed novel probe strategies and techniques for sampling shallow tissue depths.^{7, 8, 9} We present, for the first time to our knowledge, a lookup table (LUT)–based model that is valid at very short source-detector separations $(\sim 300\mu \mathrm{m})$ as well as highly absorbing media (albedo $\sim 0.35$ ). This method relies solely on a LUT generated from experimental measurements on tissue-simulating phantoms and does not depend on an analytical or computational model of light propagation. The system we used to collect the diffuse reflectance is described in detail elsewhere.¹⁰ Briefly, we used a custom-built clinical spectrometer to collect steady-state, spectrally resolved diffuse reflectance in the wavelength range of $350–700\mathrm{nm}$ . We used a (i) pulsed xenon flash lamp (L7684, Hamamatsu Photonics) as our light source, (ii) an imaging spectrograph (SP-150, Princeton Instruments) in combination with a $12\text{-}\text{bit}$ -cooled CCD (CoolSnap HQ, Photometrics) to collect diffusely reflected light, and (iii) a fiber-optic probe ( $\text{diameter}=200\mu \mathrm{m}$ ; $\mathrm{NA}=0.22$ ), where the central fiber illuminated the sample and six surrounding fibers collected the diffusely reflected light. We employed a source-detector separation of $300\mu \mathrm{m}$ (center-to-center distance). We averaged over three white-light acquisitions to improve the signal-to-noise ratio. The spectral resolution of our system is $\sim 0.78\mathrm{nm}$ . We generated the LUT by measuring the functional form of the reflectance using tissue phantoms with known optical properties (calibration set). These phantoms were fabricated using polystyrene microspheres ( $\text{diameter}=1\mu \mathrm{m}$ ; Polysciences) and India ink (Salis International) dissolved in water to simulate scattering and absorption, respectively. We used Mie theory to calculate ${\mu }_{\mathrm{s}}^{\prime }$ of the tissue phantoms and measured ${\mu }_{\mathrm{a}}$ of a stock India ink solution using a spectrophotometer (DU 720, Beckman Coulter). We created a matrix $(4\times 6)$ of 24 tissue phantoms with varying scattering $({\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)=0.22–7.1{\mathrm{mm}}^{-1})$ and absorption parameters $[{\mu }_{\mathrm{a}}\left(\lambda \right)=0–5.33{\mathrm{mm}}^{-1}]$ , based on previously reported values for tissue.¹¹ We found 24 phantoms to be sufficient for overlap in the wavelength-dependent scattering and absorption properties for different phantoms to cover the entire range of the LUT matrix. For example, for consecutive phantoms of increasing ${\mu }_{\mathrm{s}}^{\prime }$ , the lowest ${\mu }_{\mathrm{s}}^{\prime }$ of each subsequent phantom overlapped with the highest ${\mu }_{\mathrm{s}}^{\prime }$ of the previous phantom. The probe was placed in contact with the surface of the tissue phantoms, and white-light spectra from the phantoms were recorded [Fig. 1a ]. Reflectance was calculated by dividing white-light intensity measured from the phantom by white-light intensity from a reflectance standard (Labsphere). Both measurements were background corrected to account for CCD dark current and ambient light. The measurement-measurement variation in the diffuse reflectance spectra was $<2\%$ . <!-- named anchor --> Fig. 1 (a) Spectrally resolved diffuse reflectance $\left[R\left(\lambda \right)\right]$ for a subset of tissue phantoms, [ ${\mu }_{\mathrm{s}}^{\prime }\left({\lambda }_0\right)=2{\mathrm{mm}}^{-1}$ and four different ink concentrations corresponding to ${\mu }_{\mathrm{a}}\left(\lambda \right)=0–5.33{\mathrm{mm}}^{-1}$ ], from the calibration set. (b) Diffuse reflectance as a sparse matrix mapped to optical property space, $\left\{R[{\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right),{\mu }_{\mathrm{a}}\left(\lambda \right)]\right\}$ and (c) the resulting lookup table, $\left[R({\mu }_{\mathrm{s}}^{\prime },{\mu }_{\mathrm{a}})\right]$ . The mapping of the spectrally resolved diffuse reflectance $\left(R\right)$ on to a unique LUT is shown in Fig. 1. The spectral dependence of $R$ results from the wavelength-dependent optical properties, ${\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)$ and ${\mu }_{\mathrm{a}}\left(\lambda \right)$ . Because ${\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)$ and ${\mu }_{\mathrm{a}}\left(\lambda \right)$ are known for the tissue phantoms, $R$ can be mapped from wavelength space to the two-dimensional optical property space. This mapping creates a sparse matrix [Fig. 1b] for $R$ . We then interpolated this sparse matrix to a grid of uniformly spaced data points of ${\mu }_{\mathrm{s}}^{\prime }$ and ${\mu }_{\mathrm{a}}$ to obtain a LUT for diffuse reflectance [Fig. 1c]. The limits of the LUT correspond to the range of ${\mu }_{\mathrm{s}}^{\prime }$ and ${\mu }_{\mathrm{a}}$ over which diffuse reflectance spectra were recorded. Because the LUT is generated with experimental data, using the same absorber to generate the LUT as well as validate it might influence the inverse model while fitting the diffuse reflectance spectra. Therefore, we created a separate matrix $(3\times 6)$ of 18 tissue phantoms (validation set) with hemoglobin (Sigma-Aldrich) as the absorber. To fit our diffuse reflectance spectra and extract the optical properties, we implemented a nonlinear optimization fitting routine (average fit time ∼ few seconds). We constrained the reduced scattering coefficient to the form ${\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)={\mu }_{\mathrm{s}}^{\prime }\left({\lambda }_0\right).{(\lambda ∕{\lambda }_0)}^{-\mathrm{B}}$ , where ${\lambda }_0=630\mathrm{nm}$ . We assumed the absorption in the visible range to be due to oxy- and deoxy-hemoglobin. The absorption coefficient was calculated using the absorption cross-sections ( ${\sigma }_{\mathrm{Hb}}$ and ${\sigma }_{\mathrm{Hb}{\mathrm{O}}_2}$ ) of these chromophores as ${\mu }_{\mathrm{a}}\left(\lambda \right)=\left[\mathrm{Hb}\right]$ $(\alpha {\sigma }_{\mathrm{Hb}{\mathrm{O}}_2}+(1-\alpha ){\sigma }_{\mathrm{Hb}})$ , where $\alpha$ is the oxygen saturation and [Hb] is the total hemoglobin concentration. Depending on the type of tissue sampled and the wavelength range of interest, the expression for ${\mu }_{\mathrm{a}}\left(\lambda \right)$ can be modified to include the absorption cross sections of other absorbing chromophores. The diffuse reflectance spectrum and corresponding fit from a sample validation phantom is shown in Fig. 2a demonstrating excellent agreement between the model and the experimental data. Scatter plots of the extracted versus expected values of ${\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)$ [Fig. 2b] and ${\mu }_{\mathrm{a}}\left(\lambda \right)$ [Fig. 2c] demonstrate a high degree of accuracy in extracting optical properties. The LUT inverse model estimated the reduced scattering and absorption coefficients over a wide range ( ${\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)=0.72–4.91{\mathrm{mm}}^{-1}$ and ${\mu }_{\mathrm{a}}\left(\lambda \right)=0–2.29{\mathrm{mm}}^{-1}$ ) with mean root-mean-square (rms) percent errors (calculated across wavelength and phantoms) of 5.9% and 11.6%, respectively. These scatter plots show the extracted ${\mu }_{\mathrm{s}}^{\prime }\left(\lambda \right)$ and ${\mu }_{\mathrm{a}}\left(\lambda \right)$ for the entire validation set. Figures 3a and 3b illustrate extracted physical parameters for each tissue phantom of the validation set. The average errors in estimating ${\mu }_{\mathrm{s}}^{\prime }\left({\lambda }_0\right)$ and [Hb] over the entire validation set were 4.9% and 9.6%, respectively. We fit the experimental data to the LUT inverse model three times for each phantom, and the variance in the extracted parameters was $<2\%$ . All the experiments were performed immediately after preparation of the phantoms. Therefore, we did not expect the oxygen saturation values to vary, and this was evident in the fits where the values did not vary by $>2\%$ . <!-- named anchor --> Fig. 2 (a) Diffuse reflectance spectrum [