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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 4, Iss. 10 — Oct. 2, 2009
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Seven-wavelength time-resolved optical mammography extending beyond 1000 nm for breast collagen quantification

Paola Taroni, Antonio Pifferi, Elena Salvagnini, Lorenzo Spinelli, Alessandro Torricelli, and Rinaldo Cubeddu  »View Author Affiliations


Optics Express, Vol. 17, Issue 18, pp. 15932-15946 (2009)
http://dx.doi.org/10.1364/OE.17.015932


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Abstract

Our multi-wavelength time-resolved optical mammograph was upgraded to improve its overall performances and extend its spectral coverage up to 1060 nm, with the aim of increasing the measurement sensitivity to the content of collagen in breast tissue. Late-gated intensity and reduced scattering images are routinely displayed for diagnostic purposes. Maps of tissue constituents (lipid, water and collagen) and blood parameters (total hemoglobin content and blood oxygenation) are built to highlight spatial changes due to physiological and pathological reasons. The upgraded instrument was tested on tissue phantoms. Then images were collected at 7 wavelengths (635-1060 nm) from 10 healthy volunteers. Average collagen content correlated with breast density whenever x-ray mammograms were available (6 subjects).

© 2009 OSA

1. Introduction

Optical mammography performed at several wavelengths can combine the advantages that are in general offered separate by optical imaging and spectroscopy, coupling in a single non-invasive diagnostic tool the ability for lesion localization and a high potential for lesion identification. In the last years several studies were performed, typically operating at 2-4 wavelengths in the range of 680-850 nm [1

1. D. Grosenick, K. T. Moesta, M. Möller, J. Mucke, H. Wabnitz, B. Gebauer, C. Stroszczynski, B. Wassermann, P. M. Schlag, and H. Rinneberg, “Time-domain scanning optical mammography: I. Recording and assessment of mammograms of 154 patients,” Phys. Med. Biol. 50(11), 2429–2449 (2005). [CrossRef] [PubMed]

10

10. N. G. Chen, M. Huang, H. Xia, D. Piao, E. Cronin, and Q. Zhu, “Portable near-infrared diffusive light imager for breast cancer detection,” J. Biomed. Opt. 9(3), 504–510 (2004). [CrossRef] [PubMed]

]. However, two major constituents of soft tissues, namely water and lipids, are characterized by strong absorption peaks between 900 and 1000 nm. Thus, to take advantage of potential differences in water and lipid content between healthy and diseased tissue, we developed a time-resolved optical mammograph operating even at wavelengths above 900 nm (680, 785, 915, and 975 nm) and tested it in a clinical study involving 200 patients [11

11. P. Taroni, A. Torricelli, L. Spinelli, A. Pifferi, F. Arpaia, G. Danesini, and R. Cubeddu, “Time-resolved optical mammography between 637 and 985 nm: clinical study on the detection and identification of breast lesions,” Phys. Med. Biol. 50(11), 2469–2488 (2005). [CrossRef] [PubMed]

]. Few other groups included long wavelengths in their imaging systems, but for various reasons they did not take full advantage of them, not including lipid among the tissue absorbers they considered [12

12. C. Li, H. Zhao, B. Anderson, and H. Jiang, “Multispectral breast imaging using a ten-wavelength, 64 x 64 source/detector channels silicon photodiode-based diffuse optical tomography system,” Med. Phys. 33(3), 627–636 (2006). [CrossRef] [PubMed]

] or fixing the water and lipid content [13

13. S. D. Konecky, R. Choe, A. Corlu, K. Lee, R. Wiener, S. M. Srinivas, J. R. Saffer, R. Freifelder, J. S. Karp, N. Hajjioui, F. Azar, and A. G. Yodh, “Comparison of diffuse optical tomography of human breast with whole-body and breast-only positron emission tomography,” Med. Phys. 35(2), 446–455 (2008). [CrossRef] [PubMed]

]. On the contrary, recently spectroscopic studies have started to investigate spectral changes in the absorption properties of water and lipids that may occur as a consequence of cancer onset and progression [14

14. S. H. Chung, A. E. Cerussi, C. Klifa, H. M. Baek, O. Birgul, G. Gulsen, S. I. Merritt, D. Hsiang, and B. J. Tromberg, “In vivo water state measurements in breast cancer using broadband diffuse optical spectroscopy,” Phys. Med. Biol. 53(23), 6713–6727 (2008). [CrossRef] [PubMed]

,15

15. S. Kukreti, A. Cerussi, B. Tromberg, and E. Gratton, “Intrinsic tumor biomarkers revealed by novel double-differential spectroscopic analysis of near-infrared spectra,” J. Biomed. Opt. 12(2), 020509 (2007). [CrossRef] [PubMed]

].

Collagen powder is characterized by a main absorption peak at 1020-1030 nm and its absorption is significant up to at least 1100 nm [21

21. P. Taroni, D. Comelli, A. Pifferi, A. Torricelli, and R. Cubeddu, “Absorption of collagen: effects on the estimate of breast composition and related diagnostic implications,” J. Biomed. Opt. 12(1), 014021 (2007). [CrossRef] [PubMed]

]. We have recently observed that in the range of 1000-1100 nm the overall tissue absorption is lower than in the range of 900-1000 nm, thus suggesting that imaging could be performed with acceptable signal level. Still all main tissue absorbers give non-negligible contributions, as required for their quantification through absorption measurements. The only exception is deoxyhemoglobin, but its concentration can easily be estimated operating essentially at any wavelength below 900 nm.

2. Materials and methods

2.1 Instrument set-up

The instrument was designed to collect projection images of the compressed breast, in the same geometry as used for conventional x-ray mammography, but with a milder degree of compression.

The scheme is displayed in Fig. 1
Fig. 1 Instrument set-up. NIR, near-infrared; PMT, photomultiplier tube; TCSPC, time-correlated single photon counting.
. Seven pulsed diode lasers (LDH-P-XXX, PicoQuant, Germany, where XXX represents the nominal wavelength in nanometers) are presently used as light sources emitting at 635, 680, and 785 nm (VIS, visible), and at 905, 930, 975 and 1060 nm (NIR, near-infrared), with average output power of ~1-5 mW (specifically 1.2, 1.1, 1.7, 4.2, 4.7, 3.3, and 1.7 mW for increasing wavelengths), temporal width of ~150-400 ps (full width at half maximum, FWHM), and repetition rate of 20 MHz. The spectral bandwidth is <5 nm (FWHM) for VIS wavelengths, while it varies between 6 and 15 nm for NIR ones. A single driver (PDL-808 “Sepia”, PicoQuant, Germany) controls all the laser heads, and their output pulses are properly delayed by means of graded index optical fibers, and combined into a single coupler. Circular variable neutral density filters in each of the 7 illumination paths allow optimization of the illumination power at each wavelength separately. A lens produces a 5-mm diameter collimated beam that illuminates the breast. The breast is softly compressed between parallel antireflection-coated laminated glass plates. Moreover, to minimize spurious light that reaches the detection path directly, during the measurements black cloth is spread over the clear glass area. A 5.6-mm diameter, 1-m length fiber bundle collects the output light on the opposite side of the compression unit. The distal end of the bundle is bifurcated, and its two legs guide photons respectively to a photomultiplier tube (PMT) for the detection of VIS wavelengths (sensitive up to 850 nm, R5900U-01-L16, Hamamatsu, Japan) and to a PMT for NIR wavelengths (sensitive up to 1100 nm, H7422P-60, Hamamatsu, Japan). Circular variable neutral density filters, placed in front of each PMT, are used to control the illumination power during in vivo measurements and for the acquisition of the instrument response function, as the maximum attenuation provided in the illumination path (OD = 4) is not adequate and changing the settings of the laser driver would affect the laser pulse duration and stability. Furthermore, a sector of the filters is covered with a black aluminum foil and operates as a shutter. Correct positioning of light attenuators in the illumination and collection paths is achieved through computer-controlled stepper motors. In particular, for in vivo measurements the attenuators are automatically rotated to reach a pre-set number of counts (2 x 105 counts/s) at each wavelength in a reference position (i.e. close to the chest wall).

Two PC boards for time-correlated single photon counting (SPC134, Becker&Hickl, Germany) allow the acquisition of time-resolved transmittance curves at VIS and NIR wavelengths, respectively. Depending on wavelength, the instrument response function ranges between 460 and 930 ps (FWHM).

The illumination fiber and collecting bundle are scanned in tandem and a feedback on the total number of counts per point, ruled by an adjustable threshold, restricts the scan to the breast area. Independent thresholds are used for the two PMTs. The VIS PMT controls the scan, but, if the NIR threshold is reached, light to the NIR PMT can be shut off even during scanning to prevent damaging due to high signal levels. To minimize dead times, continuous acquisition is performed and data are stored every millimeter of path, i.e. every 25 ms. A complete scan with a count rate of about 106 counts/s typically requires 5 min.

The entire set-up is a stand-alone instrument (50 cm W x 80 cm D x 140 cm H), mounted on wheels and suitable for use in a clinical environment. The compression unit can be rotated by an angle up to 90° in both clock-wise and counter-clock-wise direction, so that images of both breasts can be recorded in the cranio-caudal (CC) as well as medio-lateral or oblique (OB) views.

2.2 Data analysis: imaging

Data analysis is performed with dedicated software. Images at 7 wavelengths are constructed by plotting as a function of position the number of photons collected within a selected time window as well as the reduced scattering and absorption coefficients.

To avoid dependence of gated intensity images on the temporal position and width of single transmittance curves, instead of choosing a time window of fixed delay and width, we follow the procedure described by Grosenick and colleagues [24

24. D. Grosenick, K. T. Moesta, H. Wabnitz, J. Mucke, C. Stroszczynski, R. Macdonald, P. M. Schlag, and H. Rinneberg, “Time-domain optical mammography: initial clinical results on detection and characterization of breast tumors,” Appl. Opt. 42(16), 3170–3186 (2003). [CrossRef] [PubMed]

]. Briefly, a reference position is selected far from boundaries and possible inhomogeneities. The time distribution of data acquired at that position is divided into 10 time windows, each one collecting 1/10 of the total number of counts. The same time gates are then used at any other position of the scanned area to build gated intensity images. Specifically, the 8th gate, on the tail of the pulse, is routinely applied for breast imaging.

The estimated absorption and reduced scattering coefficients (μa and μ's) are average values along the line of sight, as obtained from the best fit of the experimental data with the analytical solution of the diffusion equation, with extrapolated boundary condition, for a homogeneous infinite slab [25

25. M. S. Patterson, B. Chance, and B. C. Wilson, “Time-resolved reflectance and transmittance for the noninvasive measurement of tissue optical properties,” Appl. Opt. 28(12), 2331–2336 (1989). [CrossRef] [PubMed]

,26

26. R. C. Haskell, L. O. Svasaand, T. T. Tsay, T. C. Feng, M. S. McAdams, and B. J. Tromberg, “Boundary conditions for the diffusion equation in radiative transfer,” J. Opt. Soc. Am. A 11(10), 2727–2741 (1994). [CrossRef]

].

Late gated intensity images, which are sensitive mostly to the absorption properties, and reduced scattering images are routinely used for diagnostic purposes.

Data correction to account for “edge effects” caused by the variable thickness of the compressed breast in the boundary region presently is not performed. The “edge effects” do not affect significantly late gated intensity images, but they may hinder the detection of boundary lesions in scattering plots. However, this is not expected to limit significantly the diagnostic potential of the technique, as the acquisition of data in two different views allows us to image reliably most of the breast volume.

Based on information on tissue composition estimated as described in the following Subsection 2.3, maps are also built showing the spatial distribution of tissue constituents and blood parameters.

Images of all types are routinely displayed using the “pink” color scale (Matlab, The MathWorks Inc.), that is a linear scale, going from dark (low values) to white (high values). The pixel size is 1 mm x 1 mm, as defined by the step size of data acquisition.

2.3 Data analysis: bulk optical properties, physiological and structural parameters

For each breast projection and wavelength, the estimate of bulk optical properties is limited to a reference area that excludes boundaries and marked inhomogeneities, but still includes most of the breast. To select that area, the mean time-of-flight (i.e. the first moment of the time-resolved transmittance curve) is calculated for each image pixel, and only pixels with mean time-of-flight greater than or equal to the median of the distribution are included in the reference area. The optical properties (i.e. absorption and reduced scattering coefficients) of bulk tissue are then obtained as averages over the reference area.

Information on tissue composition and structure are obtained directly from time-resolved curves measured at 7 wavelengths. The Beer law is used to relate the absorption properties to the concentrations of the main tissue constituents. The scattering properties are modeled through a simple approximation to Mie theory: μ’s = a(λ/λo)–b, where λo = 600 nm and a is the scattering coefficient μ’so) [27

27. J. R. Mourant, T. Fuselier, J. Boyer, T. M. Johnson, and I. J. Bigio, “Predictions and measurements of scattering and absorption over broad wavelength ranges in tissue phantoms,” Appl. Opt. 36(4), 949–957 (1997). [CrossRef] [PubMed]

,28

28. A. M. Nilsson, C. Sturesson, D. L. Liu, and S. Andersson-Engels, “Changes in spectral shape of tissue optical properties in conjunction with laser-induced thermotherapy,” Appl. Opt. 37(7), 1256–1267 (1998). [CrossRef]

]. A spectrally constrained global fitting procedure is applied [29

29. C. D’Andrea, L. Spinelli, A. Bassi, A. Giusto, D. Contini, J. Swartling, A. Torricelli, and R. Cubeddu, “Time-resolved spectrally constrained method for the quantification of chromophore concentrations and scattering parameters in diffusing media,” Opt. Express 14(5), 1888–1898 (2006). [CrossRef] [PubMed]

]. Free parameters of the fit are the concentrations of oxy- and deoxy-hemoglobin (HbO2 and Hb, respectively) [30

30. S. Prahl, Oregon Medical Laser Center website http://omlc.ogi.edu/spectra/hemoglobin/index.html.

], water [31

31. L. Kou, D. Labrie, and P. Chylek, “Refractive indices of water and ice in the 0.652.5µm spectral range,” Appl. Opt. 32(19), 3531–3540 (1993). [CrossRef] [PubMed]

], lipids [32

32. R. L. P. van Veen, H. J. C. M. Sterenborg, A. Pifferi, A. Torricelli, E. Chikoidze, and R. Cubeddu, “Determination of visible near-IR absorption coefficients of mammalian fat using time- and spatially resolved diffuse reflectance and transmission spectroscopy,” J. Biomed. Opt. 10(5), 054004 (2005). [CrossRef] [PubMed]

], and collagen [23

23. P. Taroni, L. Spinelli, A. Torricelli, A. Pifferi, G. M. Danesini, and R. Cubeddu, “Multi-wavelength time domain optical mammography,” Technol. Cancer Res. Treat. 4(5), 527–538 (2005). [PubMed]

], together with the scattering amplitude a and power b.

3. Instrument upgrade and characterization

3.1 Extension of the spectral range

Our routine procedure for the localization and identification of breast lesions does not consider absorption images, due to the low spatial resolution and contrast obtained under the simplifying hypothesis of homogeneous medium. Instead, late gated intensity images proved to be sensitive to absorption changes and are routinely used to localize lesions that are characterized by different absorption properties as compared to the surrounding healthy tissue. Thus the selection of the imaging wavelengths is particularly crucial, as different wavelengths should be chosen so as to provide sensitivity to spatial changes in the concentration of distinct tissue absorbers. The original set-up of the instrument operated at 680, 785, 915, and 975 nm, wavelengths that were chosen to enhance the contributions of deoxyhemoglobin, oxyhemoglobin, lipid, and water, respectively. However, a later study showed us that shorter wavelengths could be beneficial to detect small malignant lesions through their high hemoglobin content [23

23. P. Taroni, L. Spinelli, A. Torricelli, A. Pifferi, G. M. Danesini, and R. Cubeddu, “Multi-wavelength time domain optical mammography,” Technol. Cancer Res. Treat. 4(5), 527–538 (2005). [PubMed]

]. Hence 635 nm was added. As mentioned above, 785 nm is supposed to provide information on oxygenated hemoglobin by comparison with shorter wavelengths (635 and 680 nm), that lie on the tail of the absorption peak of deoxyhemoglobin. It has always been present in our set-up, and acts as an “internal reference” for data acquired with different versions of the instrument. It also allows easy comparison with data collected by other groups, as the same or close wavelengths are generally used in optical mammography [1

1. D. Grosenick, K. T. Moesta, M. Möller, J. Mucke, H. Wabnitz, B. Gebauer, C. Stroszczynski, B. Wassermann, P. M. Schlag, and H. Rinneberg, “Time-domain scanning optical mammography: I. Recording and assessment of mammograms of 154 patients,” Phys. Med. Biol. 50(11), 2429–2449 (2005). [CrossRef] [PubMed]

,10

10. N. G. Chen, M. Huang, H. Xia, D. Piao, E. Cronin, and Q. Zhu, “Portable near-infrared diffusive light imager for breast cancer detection,” J. Biomed. Opt. 9(3), 504–510 (2004). [CrossRef] [PubMed]

]. 832 nm was also tested to increase the sensitivity to oxyhemoglobin, but it did not prove to add any significant informative content. Moreover, the detection efficiency at 832 nm is low for both the VIS and the NIR PMTs. So it was not included in the final set of wavelengths. We have often experienced low signal problems at 915 nm, which is on the raising edge of the absorption peak of lipids. Powerful diode lasers are now available at 930 nm, corresponding to the absorption maximum of lipids, and 905 nm, where lipid absorption is lower by approximately a factor of 2. Thus we decided to add both wavelengths as a possible trade-off between high selectivity and good signal to noise ratio. For a clearly adipose and thick breast, the signal level may not be adequate at 930 nm, but in that case lipid absorption is expected to be dominant at 905 nm. Moreover, as recently shown by Wang et al [33

33. J. Wang, S. Jiang, K. D. Paulsen, and B. W. Pogue, “Broadband frequency-domain near-infrared spectral tomography using a mode-locked Ti:sapphire laser,” Appl. Opt. 48(10), D198–D207 (2009). [CrossRef] [PubMed]

], adding wavelengths that are redundant as long as the visual analysis of intensity images is considered, may improve significantly the assessment of tissue composition when a spectrally constrained global fitting procedure is applied. 975 nm is on the absorption peak of water and tissue absorption at 975 nm is dominated by water, even for clearly adipose breasts.

3.2 Control of the light attenuation

In the original set-up the signal level was controlled acting on the two detection paths (VIS and NIR), with the aim of avoiding to exceed the acceptable count rate for time-correlated single-photon-counting and the damaging threshold of the detectors. Thus, all wavelengths in the same detection path underwent the same attenuation, even though typically the signal level varies considerably with wavelength. Independent optimization at each wavelength could not be performed by changing the settings of the laser driver to control the power emitted at each wavelength as this would affect the laser pulse duration and stability. Independent attenuation is now achieved through circular variable neutral density filters positioned in each of the 7 illumination paths and rotated by computer-controlled stepper-motors.

3.3 Control of the scanning area

The algorithm that determines the extension of the imaged area was improved, trying to get as close as possible to the boundary without damaging the detectors. The PMT for NIR wavelengths can be seriously damaged by exceedingly high illumination that may occur when the scan reaches locations close to the boundaries of the compressed breast. To prevent any damaging of the NIR PMT, while maximizing the scanned breast area, in the present version of the instrument the two PMTs can collect light from distinct scan areas. When, getting close to the breast boundaries, the NIR PMT reaches its threshold, the shutter in its path closes. However, the scan continues till the threshold on the number of counts of the RED PMT is reached. This allows us to scan a wider area at least at short wavelengths (<900 nm).

3.4 Improvement of the compression unit

3.5 Tests on phantoms

A latex balloon filled with an aqueous solution of Intralipid® was used as a breast tissue phantom, and in particular to mimic the critical condition that is observed with fibrous breasts at 975 nm, due to the strong water absorption. Measurements were carried out adding 5% (w/v) of Intralipid® 20% to distilled water. The balloon was compressed to a thickness of 3 cm. Both images and point measurements in the center of the area of good contact between balloon and compression plates were acquired. For point measurements acquisition times of 1 s and 8 s were tested.

The dilution of Intralipid® 20% 1:20 in distilled water resulted in a reduced scattering coefficient that decreased approximately from 15 to 10 cm−1 for increasing wavelengths. Figure 3
Fig. 3 Absorption measured from a balloon filled with Intralipid® diluted in distilled water. For comparison, the reference spectrum of water is also reported.
displays the measured absorption values. Water is expected to be the major absorber in the solution. So, as a reference, the absorption spectrum of water is also reported in the figure. On the absorption peak of water at 975 nm, the measured absorption of the Intralipid solution underestimates the absorption of water with a percentage error of no more than 20%. Below 700 nm, the measured absorption values overestimate (up to 30% at 635 nm) the absorption of water. The error in the estimate of the absorption properties can at least in part be attributed to the spectral width of the laser sources [35

35. A. Farina, A. Bassi, A. Pifferi, P. Taroni, D. Comelli, L. Spinelli, and R. Cubeddu, “Bandpass effects in time-resolved diffuse spectroscopy,” Appl. Spectrosc. 63(1), 48–56 (2009). [CrossRef] [PubMed]

] and to the duration of the instrument response function [36

36. L. Spinelli, F. Martelli, A. Farina, A. Pifferi, A. Torricelli, R. Cubeddu, and G. Zaccanti, “Accuracy of the nonlinear fitting procedure for time-resolved measurements on diffusive phantoms at NIR wavelengths,” Proc. SPIE 7174, 717424 (2009). [CrossRef]

]. However, it should be taken into account that the experimental condition tested here (3 cm of water measured at 975 nm) is extremely critical and analogous situations are hardly ever met in vivo.

Correct operation of the system and reproducibility of measured values is routinely tested at the start of each measurement session, after warm-up, measuring a solid resin phantom (thickness = 5.3 cm) with tissue-like optical properties (μa = 0.02-0.13 cm−1 and μ’s = 6-12 cm−1 in the spectral range of interest). The phantom was kindly provided by Dr. Jeremy Hebden (University College London). Eighteen measurements were repeated over a period of 9 months. The coefficient of variation of the estimated absorption and reduced scattering coefficients never exceeds 3.3% at any wavelengths.

4. Tests on volunteers

To estimate the signal level attained in case of in vivo measurements, for each image (2 views of both breasts of 10 subjects, for a total of 40 images) the number of counts per second was averaged over the reference area (defined as described in Subsection 2.3). This allowed us to exclude the border region, where high counts are artificially reached due to the decreasing thickness of the compressed breast. In general, the optimal signal level (2 x 105 counts/s) was accomplished at all wavelengths from 680 to 975 nm. At 635 nm, it was reached in 80% of the images, and the lowest signal levels were observed for fibrous breasts, possibly due at least in part to the high blood and collagen content detected in breasts of that type (as shown in the following Table 1

Table 1. Average tissue composition and scattering parameters as derived from optical data and breast pattern as derived from x-ray mammograms (following Tabàr classification [37]).

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). At 1060 nm, only 33% of the images reached the optimal signal level, with the worst situation observed for adipose breasts. The optical properties would not support such a result, as adipose breasts typically show lower absorption and reduced scattering than other breast types in the range 1000-1100 nm. The strong attenuation is likely explained by the high average thickness of the compressed breast (5.17 cm for adipose breasts as compared to 3.26 cm and 3.90 cm for fibrous and mixed breasts, respectively). However, an adequate signal level (5 x 104 counts/s) was always reached at 635 nm and in 75% of the images at 1060 nm.

The average reduced scattering coefficient decreases from about 12 to 10 cm−1 upon increasing wavelength. The corresponding images (data not shown) reveal no evident feature, in agreement with what typically observed for healthy breasts.

Actually, both constituents are expected to contribute significantly to the overall absorption of a fibrous breast at 1060 nm, as well as to its radio-opacity. Finally, strong absorption (dark areas) at 930 nm is less widespread than for the breast displayed in Fig. 4, in agreement with the different parenchymal pattern. For this breast type, various constituents (hemoglobin, water, lipid, and collagen) can give comparable contributions at 930 nm (as confirmed by the estimate of tissue composition that is discussed in the following). Thus, different from what occurs for adipose breasts, the image at 930 nm is not expected to provide clear indication of the distribution of lipids.

To derive information on average tissue composition and structure, time-resolved data at all wavelengths were globally fitted with a spectrally constrained procedure. The results obtained for the 10 volunteers are summarized in Table 1, which, for easier reading, reports the total hemoglobin content tHb and oxygen saturation SO2, instead of the concentrations of Hb and HbO2. For 6 subjects, recent x-ray mammograms were available and the breast parenchymal pattern was identified following Tabàr classification [37

37. I. T. Gram, E. Funkhouser, and L. Tabár, “The Tabár classification of mammographic parenchymal patterns,” Eur. J. Radiol. 24(2), 131–136 (1997). [CrossRef] [PubMed]

]. The remaining 4 subjects were below 40 years of age, and x-ray mammography had never been performed. Consequently, the breast type could not be assessed. We tentatively classified those breasts based on their composition for comparison with the others of known type. All subjects are listed in the Table for decreasing collagen content. This roughly corresponds to decreasing water and increasing lipid contents. It is worth noting that, even though only 6 cases can be considered, considerably lower collagen content is estimated in adipose breasts, as compared to the fibrous one, and mixed breasts have intermediate collagen content. These results suggest the potential to assess tissue composition, including collagen, and scattering parameters performing measurements at just 7 wavelengths.

From the concentrations of tissue constituents it is then possible to reconstruct the overall absorption of breast tissue. Generally the results differ by no more than 10% from absorption values estimated directly by interpretation of the time-resolved data with the diffusion equation for a homogeneous slab with extrapolated boundary conditions. The absorption properties reconstructed from tissue composition were compared also with absorption values measured with a broad band (600-1100 nm) system for time-resolved diffuse optical spectroscopy [38

38. A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007). [CrossRef] [PubMed]

,39

39. A. Bassi, A. Farina, C. D’Andrea, A. Pifferi, G. Valentini, and R. Cubeddu, “Portable, large-bandwidth time-resolved system for diffuse optical spectroscopy,” Opt. Express 15(22), 14482–14487 (2007). [CrossRef] [PubMed]

]. Examples of the comparison are reported in Fig. 6. Overall there is a good correspondence between the results obtained with the two different instruments and procedures for the interpretation of the experimental data. It is worth noting that the two sets of data were collected during the same day, but not simultaneously. Even more important, spectroscopy data are obtained from point measurements, so they may be sensitive to tissue heterogeneity and consequent spatial changes in the optical properties, while absorption data obtained with the optical mammograph are averages over the entire breast volume. Figure 6 reports the results of point measurements performed with the spectroscopy system in a central position on both breasts and averages of the values obtained with the optical mammograph in all 4 views (CC and OB of both breasts).

As shown in Table 1, for subjects #7 and #9, the estimated oxygenation level is lower than expected in physiological conditions. It is interesting to note that for the same two subjects (and only for those ones) similar values were obtained also with the broad band spectroscopy system. The x-ray mammograms for subjects #7 and #9 are not available, but their optical properties suggest a mixed breast type. This would imply a particularly heterogeneous breast tissue. A simple homogeneous model was used to derive information on tissue composition from both imaging and spectroscopy data. Thus, marked tissue heterogeneity might be responsible for the estimated artificially low oxygenation level. This problem will be further investigated in a future clinical study, when the analysis of a wide number of subjects will hopefully provide better insight.

Fig. 6 Absorption spectra of breast of subjects #10 (a) and #1 (b) as measured with a broad band spectroscopy system (open symbols) and derived from tissue composition assessed with the optical mammograph (close symbols).

The spatial distribution of tissue constituents and blood parameters as obtained from the spectrally constrained global fitting procedure are reported in Figs. 7
Fig. 7 Left breast of subject #10 in CC view. Maps of the spatial distribution of tissue constituents and blood parameters as obtained using the spectrally constrained global fitting procedure. Full scale values: lipid, 625-744 mg/cm3; water, 111-213 mg/cm3; collagen, 0-63 mg/cm3; tHb, 9.7-13.5 μM; SO2, 75-93%. The green (red) line indicates the fibrous (adipose) region for the estimate of tissue composition. The corresponding x-ray mammogram is reported on the left.
and 8
Fig. 8 Left breast of subject #1 in CC view. Maps of the spatial distribution of tissue constituents and blood parameters as obtained using the spectrally constrained global fitting procedure. Full scale values: lipid, 0-393 mg/cm3; water, 291-832 mg/cm3; collagen, 0-394 mg/cm3; tHb, 9.5-24.0 μM; SO2, 73-100%. The green (red) line indicates the fibrous (adipose) region for the estimate of tissue composition. The corresponding x-ray mammogram is reported on the left.
for subjects #10 and #1, respectively. As described previously, the imaged area is generally smaller at wavelengths >900 nm than at shorter wavelengths, and its extension identifies the region where the constituent maps are meaningful.

In Fig. 7, on average the outer quadrants (displayed in the upper part of the images) are characterized by lower lipid and higher water content, in agreement with the higher opacity to x-rays. Specifically, the position and extension of the two lighter regions in the water map (indicating high water content) resemble areas that are more radio-opaque than surrounding tissue in the x-ray mammogram. The area that is closer to the nipple has collagen content slightly higher than average. The reverse seems to occur for the other inner region, which is also characterized by lower levels of oxygen saturation than adjacent tissue.

Similar general observations can be made referring to Fig. 8. Tissue that is markedly x-ray dense appears to be characterized by high water content, while the part of the inner quadrant that is more translucent to x-rays shows higher lipid and lower water and collagen content.

By comparing Fig. 4 to Fig. 7 or Fig. 5 to Fig. 8, it is evident that constituent maps are much noisier and exhibit less spatial details than late gated intensity images. The latter essentially display the spatial changes in the number of photons detected in a selected time window. The corresponding average number of counts per pixel for the ten subjects at all wavelengths is higher than 5 x 103. The constituent maps are obtained with a fitting procedure that aims at determining 7 parameters (5 constituent concentrations and 2 scattering parameters) from time-resolved transmittance data collected at 7 wavelengths. In general, the use of a spectrally constrained global fitting procedure provides an improved stability as compared to conventional fitting methods [29

29. C. D’Andrea, L. Spinelli, A. Bassi, A. Giusto, D. Contini, J. Swartling, A. Torricelli, and R. Cubeddu, “Time-resolved spectrally constrained method for the quantification of chromophore concentrations and scattering parameters in diffusing media,” Opt. Express 14(5), 1888–1898 (2006). [CrossRef] [PubMed]

], but still a high number of parameters is fitted here. This is likely the reason for the higher noise level. The loss of spatial details observed in constituent maps is due to the fact that they are derived applying a simple homogeneous model, which provides very low spatial resolution in the estimate of the absorption properties. The estimate of tissue composition obviously shares the same limitation.

To get some information on breast heterogeneity as quantified through the constituents’ maps, the average tissue composition was estimated in selected areas. In detail, two areas were considered, characterized by higher density (i.e. more fibro-glandular tissue) or lower density (i.e. more adipose tissue) in the corresponding x-ray mammogram. In Figs. 7 and 8, the two types of areas are indicated by green lines (high density, fibrous tissue) and red lines (low density, adipose tissue). The estimated average tissue composition (reported in Table 2

Table 2. Average tissue composition in fibrous and adipose breast regions

table-icon
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) is in agreement with what expected: higher water and collagen content in fibro-glandular tissue and higher lipid content in adipose tissue. At least for the subjects we have considered up to now, blood parameters show no clear correlation with tissue type. In agreement with what shown in Table 2, in general the inter-subject tissue heterogeneity is more marked than the intra-subject one.

When analyzing the constituents’ maps, it should be taken into account that the results are obtained under the hypothesis of homogeneous medium, which, as mentioned above, is a very rough approximation. Still, some correspondence is obtained with information on tissue composition derived from conventional mammography. This seems to be a good starting point, but more realistic models need to be introduced to attain quantitative results, especially if one aims at investigating the composition of breast lesions that are often localized in small volumes. In particular, for lesion characterization a first significant advance that we are now planning to apply routinely on patients is represented by the use of perturbative approaches based on Padè approximants [40

40. A. Torricelli, L. Spinelli, A. Pifferi, P. Taroni, R. Cubeddu, and G. Danesini, “Use of a nonlinear perturbation approach for in vivo breast lesion characterization by multiwavelength time-resolved optical mammography,” Opt. Express 11(8), 853–867 (2003). [CrossRef] [PubMed]

] or diffuse photon density waves [41

41. D. Grosenick, A. Kummrow, R. Macdonald, P. M. Schlag, and H. Rinneberg, “Evaluation of higher-order time-domain perturbation theory of photon diffusion on breast-equivalent phantoms and optical mammograms,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 76(6 Pt 1), 061908 (2007). [CrossRef]

].

5. Summary and future work

In conclusion, we upgraded our time domain optical mammograph, improving the measurement quality in the presence of weak signal levels, which are expected to occur primarily for dense breasts on the water peak at 975 nm. Moreover, the spectral range of operation was extended, including 1060 nm, to provide a more accurate quantification of tissue constituents, especially collagen.

As routinely done in the past [11

11. P. Taroni, A. Torricelli, L. Spinelli, A. Pifferi, F. Arpaia, G. Danesini, and R. Cubeddu, “Time-resolved optical mammography between 637 and 985 nm: clinical study on the detection and identification of breast lesions,” Phys. Med. Biol. 50(11), 2469–2488 (2005). [CrossRef] [PubMed]

], late gated intensity images, sensitive to the absorption properties, and reduced scattering maps can be displayed for diagnostic purposes. Interpreting time distributions of the transmitted signal at all wavelengths with a spectrally constrained global fitting procedure, constituent maps are built to provide information on the spatial distribution of different tissue constituents. Based on preliminary tests performed on healthy volunteers with different breast type, average tissue composition as obtained from optical measurements seems to correlate with breast parenchymal pattern as defined from the analysis of x-ray mammograms.

The system has recently been moved to the clinic for a new trial on patients bearing malignant and benign breast lesions. First aim of the study is to characterize breast lesions, and determine if extended spectral information allows reliable estimate of tissue composition, including collagen, and if in turn this can effectively contribute to lesion differentiation. Our second goal is to define whether collagen content, as assessed through optical measurements, correlates with breast density, and more generally to achieve the non-invasive classification of breast type.

References and links

1.

D. Grosenick, K. T. Moesta, M. Möller, J. Mucke, H. Wabnitz, B. Gebauer, C. Stroszczynski, B. Wassermann, P. M. Schlag, and H. Rinneberg, “Time-domain scanning optical mammography: I. Recording and assessment of mammograms of 154 patients,” Phys. Med. Biol. 50(11), 2429–2449 (2005). [CrossRef] [PubMed]

2.

L. C. Enfield, A. P. Gibson, N. L. Everdell, D. T. Delpy, M. Schweiger, S. R. Arridge, C. Richardson, M. Keshtgar, M. Douek, and J. C. Hebden, “Three-dimensional time-resolved optical mammography of the uncompressed breast,” Appl. Opt. 46(17), 3628–3638 (2007). [CrossRef] [PubMed]

3.

X. Intes, “Time-domain optical mammography SoftScan: initial results,” Acad. Radiol. 12(8), 934–947 (2005). [CrossRef] [PubMed]

4.

V. E. Pera, E. L. Heffer, H. Siebold, O. Schütz, S. Heywang-Kobrunner, L. Götz, A. Heinig, and S. Fantini, “Spatial second-derivative image processing: an application to optical mammography to enhance the detection of breast tumors,” J. Biomed. Opt. 8(3), 517–524 (2003). [CrossRef] [PubMed]

5.

H. Dehghani, B. W. Pogue, S. P. Poplack, and K. D. Paulsen, “Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results,” Appl. Opt. 42(1), 135–145 (2003). [CrossRef] [PubMed]

6.

H. Jiang, N. V. Iftimia, Y. Xu, J. A. Eggert, L. L. Fajardo, and K. L. Klove, “Near-infrared optical imaging of the breast with model-based reconstruction,” Acad. Radiol. 9(2), 186–194 (2002). [CrossRef] [PubMed]

7.

X. Gu, Q. Zhang, M. Bartlett, L. Schutz, L. L. Fajardo, and H. Jiang, “Differentiation of cysts from solid tumors in the breast with diffuse optical tomography,” Acad. Radiol. 11(1), 53–60 (2004). [CrossRef] [PubMed]

8.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005). [CrossRef] [PubMed]

9.

Q. Zhang, T. J. Brukilacchio, A. Li, J. J. Stott, T. Chaves, E. Hillman, T. Wu, M. A. Chorlton, E. Rafferty, R. H. Moore, D. B. Kopans, and D. A. Boas, “Coregistered tomographic x-ray and optical breast imaging: initial results,” J. Biomed. Opt. 10(2), 024033 (2005). [CrossRef] [PubMed]

10.

N. G. Chen, M. Huang, H. Xia, D. Piao, E. Cronin, and Q. Zhu, “Portable near-infrared diffusive light imager for breast cancer detection,” J. Biomed. Opt. 9(3), 504–510 (2004). [CrossRef] [PubMed]

11.

P. Taroni, A. Torricelli, L. Spinelli, A. Pifferi, F. Arpaia, G. Danesini, and R. Cubeddu, “Time-resolved optical mammography between 637 and 985 nm: clinical study on the detection and identification of breast lesions,” Phys. Med. Biol. 50(11), 2469–2488 (2005). [CrossRef] [PubMed]

12.

C. Li, H. Zhao, B. Anderson, and H. Jiang, “Multispectral breast imaging using a ten-wavelength, 64 x 64 source/detector channels silicon photodiode-based diffuse optical tomography system,” Med. Phys. 33(3), 627–636 (2006). [CrossRef] [PubMed]

13.

S. D. Konecky, R. Choe, A. Corlu, K. Lee, R. Wiener, S. M. Srinivas, J. R. Saffer, R. Freifelder, J. S. Karp, N. Hajjioui, F. Azar, and A. G. Yodh, “Comparison of diffuse optical tomography of human breast with whole-body and breast-only positron emission tomography,” Med. Phys. 35(2), 446–455 (2008). [CrossRef] [PubMed]

14.

S. H. Chung, A. E. Cerussi, C. Klifa, H. M. Baek, O. Birgul, G. Gulsen, S. I. Merritt, D. Hsiang, and B. J. Tromberg, “In vivo water state measurements in breast cancer using broadband diffuse optical spectroscopy,” Phys. Med. Biol. 53(23), 6713–6727 (2008). [CrossRef] [PubMed]

15.

S. Kukreti, A. Cerussi, B. Tromberg, and E. Gratton, “Intrinsic tumor biomarkers revealed by novel double-differential spectroscopic analysis of near-infrared spectra,” J. Biomed. Opt. 12(2), 020509 (2007). [CrossRef] [PubMed]

16.

S. Alowami, S. Troup, S. Al-Haddad, I. Kirkpatrick, and P. H. Watson, “Mammographic density is related to stroma and stromal proteoglycan expression,” Breast Cancer Res. 5(5), R129–R135 (2003). [CrossRef] [PubMed]

17.

Y. P. Guo, L. J. Martin, W. Hanna, D. Banerjee, N. Miller, E. Fishell, R. Khokha, and N. F. Boyd, “Growth factors and stromal matrix proteins associated with mammographic densities,” Cancer Epidemiol. Biomarkers Prev. 10(3), 243–248 (2001). [PubMed]

18.

C. Byrne, “Studying mammographic density: implications for understanding breast cancer,” J. Natl. Cancer Inst. 89(8), 531–533 (1997). [CrossRef] [PubMed]

19.

N. F. Boyd, L. J. Martin, J. Stone, C. Greenberg, S. Minkin, and M. J. Yaffe, “Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention,” Curr. Oncol. Rep. 3(4), 314–321 (2001). [CrossRef] [PubMed]

20.

J. Couzin, “Breast cancer. Dissecting a hidden breast cancer risk,” Science 309(5741), 1664–1666 (2005). [CrossRef] [PubMed]

21.

P. Taroni, D. Comelli, A. Pifferi, A. Torricelli, and R. Cubeddu, “Absorption of collagen: effects on the estimate of breast composition and related diagnostic implications,” J. Biomed. Opt. 12(1), 014021 (2007). [CrossRef] [PubMed]

22.

P. Taroni, A. Pifferi, A. Torricelli, L. Spinelli, G. M. Danesini, and R. Cubeddu, “Do shorter wavelengths improve contrast in optical mammography?” Phys. Med. Biol. 49(7), 1203–1215 (2004). [CrossRef] [PubMed]

23.

P. Taroni, L. Spinelli, A. Torricelli, A. Pifferi, G. M. Danesini, and R. Cubeddu, “Multi-wavelength time domain optical mammography,” Technol. Cancer Res. Treat. 4(5), 527–538 (2005). [PubMed]

24.

D. Grosenick, K. T. Moesta, H. Wabnitz, J. Mucke, C. Stroszczynski, R. Macdonald, P. M. Schlag, and H. Rinneberg, “Time-domain optical mammography: initial clinical results on detection and characterization of breast tumors,” Appl. Opt. 42(16), 3170–3186 (2003). [CrossRef] [PubMed]

25.

M. S. Patterson, B. Chance, and B. C. Wilson, “Time-resolved reflectance and transmittance for the noninvasive measurement of tissue optical properties,” Appl. Opt. 28(12), 2331–2336 (1989). [CrossRef] [PubMed]

26.

R. C. Haskell, L. O. Svasaand, T. T. Tsay, T. C. Feng, M. S. McAdams, and B. J. Tromberg, “Boundary conditions for the diffusion equation in radiative transfer,” J. Opt. Soc. Am. A 11(10), 2727–2741 (1994). [CrossRef]

27.

J. R. Mourant, T. Fuselier, J. Boyer, T. M. Johnson, and I. J. Bigio, “Predictions and measurements of scattering and absorption over broad wavelength ranges in tissue phantoms,” Appl. Opt. 36(4), 949–957 (1997). [CrossRef] [PubMed]

28.

A. M. Nilsson, C. Sturesson, D. L. Liu, and S. Andersson-Engels, “Changes in spectral shape of tissue optical properties in conjunction with laser-induced thermotherapy,” Appl. Opt. 37(7), 1256–1267 (1998). [CrossRef]

29.

C. D’Andrea, L. Spinelli, A. Bassi, A. Giusto, D. Contini, J. Swartling, A. Torricelli, and R. Cubeddu, “Time-resolved spectrally constrained method for the quantification of chromophore concentrations and scattering parameters in diffusing media,” Opt. Express 14(5), 1888–1898 (2006). [CrossRef] [PubMed]

30.

S. Prahl, Oregon Medical Laser Center website http://omlc.ogi.edu/spectra/hemoglobin/index.html.

31.

L. Kou, D. Labrie, and P. Chylek, “Refractive indices of water and ice in the 0.652.5µm spectral range,” Appl. Opt. 32(19), 3531–3540 (1993). [CrossRef] [PubMed]

32.

R. L. P. van Veen, H. J. C. M. Sterenborg, A. Pifferi, A. Torricelli, E. Chikoidze, and R. Cubeddu, “Determination of visible near-IR absorption coefficients of mammalian fat using time- and spatially resolved diffuse reflectance and transmission spectroscopy,” J. Biomed. Opt. 10(5), 054004 (2005). [CrossRef] [PubMed]

33.

J. Wang, S. Jiang, K. D. Paulsen, and B. W. Pogue, “Broadband frequency-domain near-infrared spectral tomography using a mode-locked Ti:sapphire laser,” Appl. Opt. 48(10), D198–D207 (2009). [CrossRef] [PubMed]

34.

O. Steinkellner, A. Hagen, C. Stadelhoff, D. Grosenick, R. Macdonald, H. Rinneberg, R. Ziegler, and T. Nielsen, “Recording of Artifact-Free Reflection Data with a Laser and Fluorescence Scanning Mammograph for Improved Axial Resolution”, in Biomedical Optics/Digital Holography and Three-Dimensional Imaging/Laser Applications to Chemical, Security and Environmental Analysis on CD-ROM (The Optical Society of America, Washington, DC, 2008), BMD45.

35.

A. Farina, A. Bassi, A. Pifferi, P. Taroni, D. Comelli, L. Spinelli, and R. Cubeddu, “Bandpass effects in time-resolved diffuse spectroscopy,” Appl. Spectrosc. 63(1), 48–56 (2009). [CrossRef] [PubMed]

36.

L. Spinelli, F. Martelli, A. Farina, A. Pifferi, A. Torricelli, R. Cubeddu, and G. Zaccanti, “Accuracy of the nonlinear fitting procedure for time-resolved measurements on diffusive phantoms at NIR wavelengths,” Proc. SPIE 7174, 717424 (2009). [CrossRef]

37.

I. T. Gram, E. Funkhouser, and L. Tabár, “The Tabár classification of mammographic parenchymal patterns,” Eur. J. Radiol. 24(2), 131–136 (1997). [CrossRef] [PubMed]

38.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007). [CrossRef] [PubMed]

39.

A. Bassi, A. Farina, C. D’Andrea, A. Pifferi, G. Valentini, and R. Cubeddu, “Portable, large-bandwidth time-resolved system for diffuse optical spectroscopy,” Opt. Express 15(22), 14482–14487 (2007). [CrossRef] [PubMed]

40.

A. Torricelli, L. Spinelli, A. Pifferi, P. Taroni, R. Cubeddu, and G. Danesini, “Use of a nonlinear perturbation approach for in vivo breast lesion characterization by multiwavelength time-resolved optical mammography,” Opt. Express 11(8), 853–867 (2003). [CrossRef] [PubMed]

41.

D. Grosenick, A. Kummrow, R. Macdonald, P. M. Schlag, and H. Rinneberg, “Evaluation of higher-order time-domain perturbation theory of photon diffusion on breast-equivalent phantoms and optical mammograms,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 76(6 Pt 1), 061908 (2007). [CrossRef]

OCIS Codes
(170.3830) Medical optics and biotechnology : Mammography
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.5280) Medical optics and biotechnology : Photon migration
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(170.6935) Medical optics and biotechnology : Tissue characterization

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: June 22, 2009
Revised Manuscript: July 24, 2009
Manuscript Accepted: August 13, 2009
Published: August 24, 2009

Virtual Issues
Vol. 4, Iss. 10 Virtual Journal for Biomedical Optics

Citation
Paola Taroni, Antonio Pifferi, Elena Salvagnini, Lorenzo Spinelli, Alessandro Torricelli, and Rinaldo Cubeddu, "Seven-wavelength time-resolved optical mammography extending beyond 1000 nm for breast collagen quantification," Opt. Express 17, 15932-15946 (2009)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-17-18-15932


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References

  1. D. Grosenick, K. T. Moesta, M. Möller, J. Mucke, H. Wabnitz, B. Gebauer, C. Stroszczynski, B. Wassermann, P. M. Schlag, and H. Rinneberg, “Time-domain scanning optical mammography: I. Recording and assessment of mammograms of 154 patients,” Phys. Med. Biol. 50(11), 2429–2449 (2005). [CrossRef] [PubMed]
  2. L. C. Enfield, A. P. Gibson, N. L. Everdell, D. T. Delpy, M. Schweiger, S. R. Arridge, C. Richardson, M. Keshtgar, M. Douek, and J. C. Hebden, “Three-dimensional time-resolved optical mammography of the uncompressed breast,” Appl. Opt. 46(17), 3628–3638 (2007). [CrossRef] [PubMed]
  3. X. Intes, “Time-domain optical mammography SoftScan: initial results,” Acad. Radiol. 12(8), 934–947 (2005). [CrossRef] [PubMed]
  4. V. E. Pera, E. L. Heffer, H. Siebold, O. Schütz, S. Heywang-Kobrunner, L. Götz, A. Heinig, and S. Fantini, “Spatial second-derivative image processing: an application to optical mammography to enhance the detection of breast tumors,” J. Biomed. Opt. 8(3), 517–524 (2003). [CrossRef] [PubMed]
  5. H. Dehghani, B. W. Pogue, S. P. Poplack, and K. D. Paulsen, “Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results,” Appl. Opt. 42(1), 135–145 (2003). [CrossRef] [PubMed]
  6. H. Jiang, N. V. Iftimia, Y. Xu, J. A. Eggert, L. L. Fajardo, and K. L. Klove, “Near-infrared optical imaging of the breast with model-based reconstruction,” Acad. Radiol. 9(2), 186–194 (2002). [CrossRef] [PubMed]
  7. X. Gu, Q. Zhang, M. Bartlett, L. Schutz, L. L. Fajardo, and H. Jiang, “Differentiation of cysts from solid tumors in the breast with diffuse optical tomography,” Acad. Radiol. 11(1), 53–60 (2004). [CrossRef] [PubMed]
  8. R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005). [CrossRef] [PubMed]
  9. Q. Zhang, T. J. Brukilacchio, A. Li, J. J. Stott, T. Chaves, E. Hillman, T. Wu, M. A. Chorlton, E. Rafferty, R. H. Moore, D. B. Kopans, and D. A. Boas, “Coregistered tomographic x-ray and optical breast imaging: initial results,” J. Biomed. Opt. 10(2), 024033 (2005). [CrossRef] [PubMed]
  10. N. G. Chen, M. Huang, H. Xia, D. Piao, E. Cronin, and Q. Zhu, “Portable near-infrared diffusive light imager for breast cancer detection,” J. Biomed. Opt. 9(3), 504–510 (2004). [CrossRef] [PubMed]
  11. P. Taroni, A. Torricelli, L. Spinelli, A. Pifferi, F. Arpaia, G. Danesini, and R. Cubeddu, “Time-resolved optical mammography between 637 and 985 nm: clinical study on the detection and identification of breast lesions,” Phys. Med. Biol. 50(11), 2469–2488 (2005). [CrossRef] [PubMed]
  12. C. Li, H. Zhao, B. Anderson, and H. Jiang, “Multispectral breast imaging using a ten-wavelength, 64 x 64 source/detector channels silicon photodiode-based diffuse optical tomography system,” Med. Phys. 33(3), 627–636 (2006). [CrossRef] [PubMed]
  13. S. D. Konecky, R. Choe, A. Corlu, K. Lee, R. Wiener, S. M. Srinivas, J. R. Saffer, R. Freifelder, J. S. Karp, N. Hajjioui, F. Azar, and A. G. Yodh, “Comparison of diffuse optical tomography of human breast with whole-body and breast-only positron emission tomography,” Med. Phys. 35(2), 446–455 (2008). [CrossRef] [PubMed]
  14. S. H. Chung, A. E. Cerussi, C. Klifa, H. M. Baek, O. Birgul, G. Gulsen, S. I. Merritt, D. Hsiang, and B. J. Tromberg, “In vivo water state measurements in breast cancer using broadband diffuse optical spectroscopy,” Phys. Med. Biol. 53(23), 6713–6727 (2008). [CrossRef] [PubMed]
  15. S. Kukreti, A. Cerussi, B. Tromberg, and E. Gratton, “Intrinsic tumor biomarkers revealed by novel double-differential spectroscopic analysis of near-infrared spectra,” J. Biomed. Opt. 12(2), 020509 (2007). [CrossRef] [PubMed]
  16. S. Alowami, S. Troup, S. Al-Haddad, I. Kirkpatrick, and P. H. Watson, “Mammographic density is related to stroma and stromal proteoglycan expression,” Breast Cancer Res. 5(5), R129–R135 (2003). [CrossRef] [PubMed]
  17. Y. P. Guo, L. J. Martin, W. Hanna, D. Banerjee, N. Miller, E. Fishell, R. Khokha, and N. F. Boyd, “Growth factors and stromal matrix proteins associated with mammographic densities,” Cancer Epidemiol. Biomarkers Prev. 10(3), 243–248 (2001). [PubMed]
  18. C. Byrne, “Studying mammographic density: implications for understanding breast cancer,” J. Natl. Cancer Inst. 89(8), 531–533 (1997). [CrossRef] [PubMed]
  19. N. F. Boyd, L. J. Martin, J. Stone, C. Greenberg, S. Minkin, and M. J. Yaffe, “Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention,” Curr. Oncol. Rep. 3(4), 314–321 (2001). [CrossRef] [PubMed]
  20. J. Couzin, “Breast cancer. Dissecting a hidden breast cancer risk,” Science 309(5741), 1664–1666 (2005). [CrossRef] [PubMed]
  21. P. Taroni, D. Comelli, A. Pifferi, A. Torricelli, and R. Cubeddu, “Absorption of collagen: effects on the estimate of breast composition and related diagnostic implications,” J. Biomed. Opt. 12(1), 014021 (2007). [CrossRef] [PubMed]
  22. P. Taroni, A. Pifferi, A. Torricelli, L. Spinelli, G. M. Danesini, and R. Cubeddu, “Do shorter wavelengths improve contrast in optical mammography?” Phys. Med. Biol. 49(7), 1203–1215 (2004). [CrossRef] [PubMed]
  23. P. Taroni, L. Spinelli, A. Torricelli, A. Pifferi, G. M. Danesini, and R. Cubeddu, “Multi-wavelength time domain optical mammography,” Technol. Cancer Res. Treat. 4(5), 527–538 (2005). [PubMed]
  24. D. Grosenick, K. T. Moesta, H. Wabnitz, J. Mucke, C. Stroszczynski, R. Macdonald, P. M. Schlag, and H. Rinneberg, “Time-domain optical mammography: initial clinical results on detection and characterization of breast tumors,” Appl. Opt. 42(16), 3170–3186 (2003). [CrossRef] [PubMed]
  25. M. S. Patterson, B. Chance, and B. C. Wilson, “Time-resolved reflectance and transmittance for the noninvasive measurement of tissue optical properties,” Appl. Opt. 28(12), 2331–2336 (1989). [CrossRef] [PubMed]
  26. R. C. Haskell, L. O. Svasaand, T. T. Tsay, T. C. Feng, M. S. McAdams, and B. J. Tromberg, “Boundary conditions for the diffusion equation in radiative transfer,” J. Opt. Soc. Am. A 11(10), 2727–2741 (1994). [CrossRef]
  27. J. R. Mourant, T. Fuselier, J. Boyer, T. M. Johnson, and I. J. Bigio, “Predictions and measurements of scattering and absorption over broad wavelength ranges in tissue phantoms,” Appl. Opt. 36(4), 949–957 (1997). [CrossRef] [PubMed]
  28. A. M. Nilsson, C. Sturesson, D. L. Liu, and S. Andersson-Engels, “Changes in spectral shape of tissue optical properties in conjunction with laser-induced thermotherapy,” Appl. Opt. 37(7), 1256–1267 (1998). [CrossRef]
  29. C. D’Andrea, L. Spinelli, A. Bassi, A. Giusto, D. Contini, J. Swartling, A. Torricelli, and R. Cubeddu, “Time-resolved spectrally constrained method for the quantification of chromophore concentrations and scattering parameters in diffusing media,” Opt. Express 14(5), 1888–1898 (2006). [CrossRef] [PubMed]
  30. S. Prahl, Oregon Medical Laser Center website http://omlc.ogi.edu/spectra/hemoglobin/index.html .
  31. L. Kou, D. Labrie, and P. Chylek, “Refractive indices of water and ice in the 0.652.5µm spectral range,” Appl. Opt. 32(19), 3531–3540 (1993). [CrossRef] [PubMed]
  32. R. L. P. van Veen, H. J. C. M. Sterenborg, A. Pifferi, A. Torricelli, E. Chikoidze, and R. Cubeddu, “Determination of visible near-IR absorption coefficients of mammalian fat using time- and spatially resolved diffuse reflectance and transmission spectroscopy,” J. Biomed. Opt. 10(5), 054004 (2005). [CrossRef] [PubMed]
  33. J. Wang, S. Jiang, K. D. Paulsen, and B. W. Pogue, “Broadband frequency-domain near-infrared spectral tomography using a mode-locked Ti:sapphire laser,” Appl. Opt. 48(10), D198–D207 (2009). [CrossRef] [PubMed]
  34. O. Steinkellner, A. Hagen, C. Stadelhoff, D. Grosenick, R. Macdonald, H. Rinneberg, R. Ziegler, and T. Nielsen, “Recording of Artifact-Free Reflection Data with a Laser and Fluorescence Scanning Mammograph for Improved Axial Resolution”, in Biomedical Optics/Digital Holography and Three-Dimensional Imaging/Laser Applications to Chemical, Security and Environmental Analysis on CD-ROM (The Optical Society of America, Washington, DC, 2008), BMD45.
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