JBO Letters

Cost-effective diffuse reflectance spectroscopy device for quantifying tissue absorption and scattering in vivo

[+] Author Affiliations
Bing Yu, Justin Y. Lo, Janelle E. Bender, Nirmala Ramanujam

Duke University, Department of Biomedical Engineering, Durham, North Carolina 27708

Thomas F. Kuech

University of Wisconsin, Department of Chemical and Biological Engineering, Madison, Wisconsin 53706

Gregory M. Palmer

Duke University, Department of Radiation Oncology, Durham, North Carolina 27708

J. Biomed. Opt. 13(6), 060505 (December 15, 2008). doi:10.1117/1.3041500
History: Received July 15, 2008; Revised September 24, 2008; Accepted October 17, 2008; Published December 15, 2008
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* Address all correspondence to: Bing Yu, Dept. Biomedical Engineering, Duke University, 136 Hudson Hall, Box 90281, Durham, NC 27708-0281. Tel: 919-660-5033; E-mail: bing.yu@duke.edu

A hybrid optical device that uses a multimode fiber coupled to a tunable light source for illumination and a 2.4-mm photodiode for detection in contact with the tissue surface is developed as a first step toward our goal of developing a cost-effective, miniature spectral imaging device to map tissue optical properties in vivo. This device coupled with an inverse Monte Carlo model of reflectance is demonstrated to accurately quantify tissue absorption and scattering in tissue-like turbid synthetic phantoms with a wide range of optical properties. The overall errors for quantifying the absorption and scattering coefficients are 6.0±5.6 and 6.1±4.7%, respectively. Compared with fiber-based detection, having the detector right at the tissue surface can significantly improve light collection efficiency, thus reducing the requirement for sophisticated detectors with high sensitivity, and this design can be easily expanded into a quantitative spectral imaging system for mapping tissue optical properties in vivo.

Figures in this Article

UV-visible diffuse reflectance spectroscopy (UV-VIS DRS) is sensitive to the absorption and scattering properties of biological molecules in tissue and thus can be used as a tool for quantitative tissue physiology in vivo. One major absorber of light in mucosal tissue in the visible range is hemoglobin (Hb), which shows distinctive, wavelength-dependent absorbance characteristics depending on its concentration and oxygenation. Tissue scattering is sensitive to the size and density of cellular structures such as nuclei and mitochondria. Thus, DRS of tissues can quantify changes in oxygenation, blood volume and alterations in cellular density and morphology. Some potential clinical applications of UV-VIS DRS include monitoring of tissue oxygenation,1 precancer and cancer detection,23 intraoperative tumor margin assessment,4 and assessing tumor response to cancer therapy.1

Our group has developed a fiber optic DRS system5 and a fast inverse Monte Carlo (MC) model of reflectance6 to nondestructively and rapidly quantify tissue absorption and scattering properties. The system consists of a 450-W xenon lamp, a monochromator, a fiber optic probe, an imaging spectrograph, and a CCD camera. Previously published studies by our group7 show that this technology is capable of quantifying breast tissue physiological and morphological properties, and that these quantities can be used to discern between malignant and non-malignant tissues with sensitivities and specificities exceeding 80%. Although this technology coupled with the MC model is a robust toolbox for quantifying tissue optical properties, this system suffers from several drawbacks similar to other spectrometers. First, optical fibers when used for detection, collect a relatively small portion of the remitted signal, thus high-quantum-efficiency, low-noise detectors are required to detect the signal, particularly in the UV-blue spectral region. Optical-fiber-based detection, while reasonable for single-point sampling, is unwieldy and expensive when expanded for use in imaging applications. Thus, a simpler, low-cost, and more portable reflectance spectrometer, capable of making fast measurements and easily extendable into a spectral imaging platform for mapping tissue optical properties is desirable for clinical applications. Previous studies have attempted to develop a portable DRS probe for cancer detection. Cerussi et al. 8 developed a handheld (5×8×10cm) laser breast scanner (LBS) based on frequency-domain near-infrared spectroscopy for breast cancer detection. The LBS probe consists of a fiber bundle for illumination and an avalanche photodiode module placed 22mm from the fiber bundle for detection. Feather et al. 9 reported a portable diffuse reflectometer that uses nine LEDs at three visible wavelengths to illuminate skin and a photodiode to collect diffusely reflected light, through a 7-mm aperture. The LBS has a sensing depth over 1cm, but is difficult to multiplex into a spectral imaging device because of the size of the device. The LED-photodiode-based reflectometer is extendable to imaging, but measurements based on this device does not provide quantitative endpoints such as absorption and scattering, which relate to the underlying biology of the tissue.

Our long-term goal is to develop a cost-effective, miniature spectral imaging device for quantifying tumor physiology and morphology with performance comparable to its benchtop counterpart. In this letter, we describe a single-point hybrid optical probe that consists of a multimode illumination fiber and a silicon photodiode as a first step toward the long-term goal. We demonstrate that diffuse reflectance (DR) spectra measured with the hybrid system coupled with our inverse MC model provides quantitative measures of tissue absorption and scattering with accuracy that is comparable to that of the original benchtop system.

The hybrid system, shown in Fig. 1, consists of a 450-W xenon lamp and monochromator (JY Horiba, Edison, New Jersey), a 1-mm illumination optical fiber [numerical aperture (NA)=0.22], a 2.4-mm silicon photodiode (S1226, Hamamatsu, Japan) with a low-noise current amplifier (PDA-750, Terahertz Technologies Inc., Oriskany, New York), and a laptop computer. The hybrid system uses the same light source and monochromator and an illumination fiber with similar diameter and NA as the original system. The primary difference between the two systems is that the photodiode and current amplifier in the new system replace the collection fibers, spectrograph, and CCD camera in the original system. At the distal end of the probe [Fig. 1] the edge of the photodiode was trimmed to the active area and transparent epoxy was used to bond the cleaved fiber adjacent to the photodiode, such that the center-to-center distance between the fiber and the photodiode is 2.1mm. The overall diameter of the probe tip is 6mm. The maximum power out of the illumination fiber was 130μW at 470nm, and the minimum power was 65μW at 590nm. This system has significantly lower cost and better collection efficiency than the original system because of the larger NA of the silicon photodiode (NA=0.96) and its direct contact with the sample. It can also be easily multiplexed into a spectral imaging device by interfacing a bundle of optical fibers to the exit slit of the monochromator and separating the fibers at the distal end, such that each fiber is coupled to a discrete photodiode within a large matrix of photodiodes.

Graphic Jump LocationF1 :

Schematic of (a) modified spectrometer and (b) probe tip.

To evaluate the performance of the modified system, a series of experiments were conducted on homogeneous tissue phantoms. Prior to the phantom experiments, the long-term drift and signal-to-noise ratio (SNR) of the system were characterized. We determined that the drift of the system was less than 1nA over 2h with the lamp on and the probe tip in contact with the surface of a liquid phantom. By taking three consecutive DR spectra from 400to600nm in the darkest phantom among the 10 phantoms described in the following, we calculated an average SNR [=20log(meanintensitystandarddeviation)] of 42.9dB over all wavelengths and a minimum SNR of 24.6dB at 410nm, which is close to the Soret band of oxy-Hb.

Phantoms with absorption coefficient (μa) and reduced scattering coefficient (μs) representative of human breast tissues in the 400to600-nm wavelength range6 were created with the scatterer, 1-μm-diam polystyrene spheres (07310-15, Polysciences, Inc., Warrington, Pennsylvania) and variable concentrations of the absorber, Hb (H0267, Sigma Co., St. Louis, Missouri). Two sets of liquid phantoms were created by titrating the absorber at two scattering levels, and all DR measurements were made the day the phantoms were prepared. The first set of phantoms (1A to 1E) consisted of five low-scattering phantoms (wavelength-averaged μs10.6cm1) with wavelength-averaged μa of 0.49, 0.88, 1.28, 1.58, and 1.97cm1 over the 400to600-nm range. The second set (2A to 2E) consisted of five high-scattering phantoms (wavelength-averaged μs18.5cm1) with the same μa values as the first set. A complete DR spectrum was collected from each phantom by scanning the bandpass of the monochromator (4.5nm) from 400to600nm at increments of 5nm. Then, a DR spectrum was also obtained from a Spectralon 99% diffuse reflectance puck (SRS-99-010, Labsphere, Inc., North Sutton, New Hampshire) with the probe in contact with the puck immediately after the phantom measurements with the same instrument settings.

An inverse MC model6 was used to extract the μa and μs of the liquid phantoms. The model was validated in both phantom6,10 and clinical studies.7 The MC forward model assumes a set of absorbers (oxy-Hb with known extinction coefficients measured using a spectrophotometer in this case) are present in the medium. The scatterer (polystyrene microsphere in this study) is assumed to be single-sized, spherically shaped, and uniformly distributed. The μa(λ) of the medium are calculated from the concentration of each absorber and the corresponding extinction coefficients using Beer’s law. The μs(λ) and anisotropy factor are calculated using Mie theory.11 The μa(λ) and μs(λ) are then input into a scalable MC model of light transport to obtain a modeled DRs spectrum. In the inverse model, the modeled DR is adaptively fitted to the measured tissue DR. When the sum of square error between the modeled and measured DR is minimized, the concentrations of absorber, from which μa can be derived, and μs are extracted. To experimentally compare measured phantom spectra to MC simulated phantom spectra for the fitting process, the “calibrated” DR spectrum of the target phantom for which the optical properties are to be quantified, was divided point by point by the “calibrated” DR spectrum of a reference phantom with known optical properties. The term “calibrated” in both cases refers to the normalization of the DR spectrum to that measured from the Spectralon puck for correction of the wavelength-dependent response of the instrument. In this phantom study, phantom 1C (wavelength-averaged μa=1.28cm1, wavelength-averaged μs=10.6cm1) was selected as a reference phantom and the remaining nine phantoms were used as targets. Bender et al. previously provided guidelines for the selection of a reference phantom.10

Figure 2 shows the Spectralon puck-calibrated reflectance spectra for two phantoms 1A and 1E and the corresponding fits to the MC model. The three valleys at 415, 540, and 575nm on the spectra for both phantoms are the Soret band (400to450nm), α band (540nm), and β band (569nm) of oxygenated Hb, respectively. There is excellent agreement between the measured spectra and the fits. Figures 3 show the extracted versus expected μa and μs for all wavelengths over the 400to600-nm range quantified with the modified and original systems for the similar range of optical properties. The 10 phantoms tested with the modified system have an overall μa range of 0.035to10cm1 and a μs range of 9.2to22.2cm1, while that tested with the original system have overall μa and μs ranges of 0.008to16.0cm1 and 9.3to23.2cm1, respectively. The reference phantom used for measurements made with the original system had a wavelength-averaged μa=2.0cm1 and μs=10.6cm1. The correlation coefficients for μa and μs are 0.9981 and 0.9588, respectively, for optical properties quantified with the modified system. An overall error of 6.0±5.6% was calculated for μa and 6.1±4.7% for μs for the modified system. For the purposes of comparison, the original system had a overall errors 5.8±5.1 and 3.0±3.1% for extracting μa and μs, respectively.

Graphic Jump LocationF2 :

Calibrated measured and MC-fitted tissue phantom spectra.

Graphic Jump LocationF3 :

Extracted versus expected (a) absorption coefficient and (b) reduced scattering coefficient.

The modified system can quantify absorption from phantoms with modest absorption coefficients (up to 10cm1). Compared to the original system, the modified system has higher errors in extraction of scattering coefficient due to its 10to15-dB lower SNR for high scattering. The dynamic range of the system may be improved by decreasing the center-to-center distance between the source and detector as well as by increasing the area of the photodiode.

We believe that the modified system combined with our MC model can be extended into an optical spectral imaging system to map out the concentrations of absorbers and the bulk tissue scattering properties of subsurface tissue volumes, which are on a length scale of several millimeters. There are a number of applications for which this technology would be ideally suited, including epithelial precancer and cancer detection (such as those of the skin, oral cavity, and cervix), intraoperative tumor margin assessment, and the monitoring of tumor response to therapy in organ sites such as the head and neck and cervix. More importantly, placing the detector directly at the tissue surface will improve collection efficiency and will significantly reduce the cost associated with expensive and sophisticated CCDs.

Acknowledgments

This work has been funded by a Department of Defense Breast Cancer Research Program (DOD BRCP) Era of Hope Scholar award to Dr. Nirmala Ramanujam.

Bigio  I. J., and Bown  S. G., “ Spectroscopic sensing of cancer and cancer therapy, current status of translational research. ,” Cancer Biother.  1062-8401 3, (3 ), 259–267  ((2004)).
Zonios  G., , Perelman  L. T., , Backman  V., , Manoharan  R., , Fitzmaurice  M., , Van Dam  J., , and Feld  M. S., “ Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo. ,” Appl. Opt..  0003-6935 38, (31 ), 6628–6637  ((1999)).
Mirabal  Y. N., , Chang  S. K., , Atkinson  E. N., , Malpica  A., , Follen  M., , and Richards-Kortum  R., “ Reflectance spectroscopy for in vivo detection of cervical precancer. ,” J. Biomed. Opt..  1083-3668 7, (4 ), 587–594  ((2002)).
Lin  W. C., , Toms  S. A., , Johnson  M., , Jansen  E. D., , and Mahadevan-Jansen  A., “ In vivo brain tumor demarcation using optical spectroscopy. ,” Photochem. Photobiol..  0031-8655 73, (4 ), 396–402  ((2001)).
Zhu  C., , Palmer  G. M., , Breslin  T. M., , Xu  F., , and Ramanujam  N., “ Use of a multiseparation fiber optic probe for the optical diagnosis of breast cancer. ,” J. Biomed. Opt..  1083-3668 10, (2 ), 024032  ((2005)).
Palmer  G. M., and Ramanujam  N., “ Monte Carlo-based inverse model for calculating tissue optical properties. Part I, Theory and validation on synthetic phantoms. ,” Appl. Opt..  0003-6935 45, (5 ), 1062–1071  ((2006)).
Zhu  C., , Palmer  G. M., , Breslin  T. M., , Harter  J., , and Ramanujam  N., “ Diagnosis of breast cancer using diffuse reflectance spectroscopy: comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique. ,” Lasers Surg. Med..  0196-8092 38, (7 ), 714–724  ((2006)).
Cerussi  A., , Shah  N., , Hsiang  D., , Durkin  A., , Butler  J., , and Tromberg  B. J., “ In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. ,” J. Biomed. Opt..  1083-3668 11, (4 ), 044005  ((2006)).
Feather  J. W., , Ellis  D. J., , and Leslie  G., “ A portable reflectometer for the rapid quantification of cutaneous haemoglobin and melanin. ,” Phys. Med. Biol..  0031-9155 33, (6 ), 711–722  ((1988)).
Bender  J. E., , Vishwanath  K., , Moore  L. K., , Brown  J. Q., , Chang  V., , Palmer  G. M., , and Ramanujam  N., “ A robust Monte Carlo model for the extraction of biological absorption and scattering in vivo. ,” IEEE Trans. Biomed. Eng..  0018-9294  (in press).
Huffman  B. C. F.,  Absorption and Scattering of Light by Small Particles. ,  Wiley ,  New York  ((1998)).
© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

Bing Yu ; Justin Y. Lo ; Thomas F. Kuech ; Gregory M. Palmer ; Janelle E. Bender, et al.
"Cost-effective diffuse reflectance spectroscopy device for quantifying tissue absorption and scattering in vivo", J. Biomed. Opt. 13(6), 060505 (December 15, 2008). ; http://dx.doi.org/10.1117/1.3041500


Figures

Graphic Jump LocationF1 :

Schematic of (a) modified spectrometer and (b) probe tip.

Graphic Jump LocationF2 :

Calibrated measured and MC-fitted tissue phantom spectra.

Graphic Jump LocationF3 :

Extracted versus expected (a) absorption coefficient and (b) reduced scattering coefficient.

Tables

References

Bigio  I. J., and Bown  S. G., “ Spectroscopic sensing of cancer and cancer therapy, current status of translational research. ,” Cancer Biother.  1062-8401 3, (3 ), 259–267  ((2004)).
Zonios  G., , Perelman  L. T., , Backman  V., , Manoharan  R., , Fitzmaurice  M., , Van Dam  J., , and Feld  M. S., “ Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo. ,” Appl. Opt..  0003-6935 38, (31 ), 6628–6637  ((1999)).
Mirabal  Y. N., , Chang  S. K., , Atkinson  E. N., , Malpica  A., , Follen  M., , and Richards-Kortum  R., “ Reflectance spectroscopy for in vivo detection of cervical precancer. ,” J. Biomed. Opt..  1083-3668 7, (4 ), 587–594  ((2002)).
Lin  W. C., , Toms  S. A., , Johnson  M., , Jansen  E. D., , and Mahadevan-Jansen  A., “ In vivo brain tumor demarcation using optical spectroscopy. ,” Photochem. Photobiol..  0031-8655 73, (4 ), 396–402  ((2001)).
Zhu  C., , Palmer  G. M., , Breslin  T. M., , Xu  F., , and Ramanujam  N., “ Use of a multiseparation fiber optic probe for the optical diagnosis of breast cancer. ,” J. Biomed. Opt..  1083-3668 10, (2 ), 024032  ((2005)).
Palmer  G. M., and Ramanujam  N., “ Monte Carlo-based inverse model for calculating tissue optical properties. Part I, Theory and validation on synthetic phantoms. ,” Appl. Opt..  0003-6935 45, (5 ), 1062–1071  ((2006)).
Zhu  C., , Palmer  G. M., , Breslin  T. M., , Harter  J., , and Ramanujam  N., “ Diagnosis of breast cancer using diffuse reflectance spectroscopy: comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique. ,” Lasers Surg. Med..  0196-8092 38, (7 ), 714–724  ((2006)).
Cerussi  A., , Shah  N., , Hsiang  D., , Durkin  A., , Butler  J., , and Tromberg  B. J., “ In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. ,” J. Biomed. Opt..  1083-3668 11, (4 ), 044005  ((2006)).
Feather  J. W., , Ellis  D. J., , and Leslie  G., “ A portable reflectometer for the rapid quantification of cutaneous haemoglobin and melanin. ,” Phys. Med. Biol..  0031-9155 33, (6 ), 711–722  ((1988)).
Bender  J. E., , Vishwanath  K., , Moore  L. K., , Brown  J. Q., , Chang  V., , Palmer  G. M., , and Ramanujam  N., “ A robust Monte Carlo model for the extraction of biological absorption and scattering in vivo. ,” IEEE Trans. Biomed. Eng..  0018-9294  (in press).
Huffman  B. C. F.,  Absorption and Scattering of Light by Small Particles. ,  Wiley ,  New York  ((1998)).

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