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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 16, Iss. 12 — Jun. 9, 2008
  • pp: 8581–8593
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Fluorescence tomography characterization for sub-surface imaging with protoporphyrin IX

Dax Kepshire, Scott C. Davis, Hamid Dehghani, Keith D. Paulsen, and Brian W. Pogue  »View Author Affiliations


Optics Express, Vol. 16, Issue 12, pp. 8581-8593 (2008)
http://dx.doi.org/10.1364/OE.16.008581


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Abstract

Optical imaging of fluorescent objects embedded in a tissue simulating medium was characterized using non-contact based approaches to fluorescence remittance imaging (FRI) and sub-surface fluorescence diffuse optical tomography (FDOT). Using Protoporphyrin IX as a fluorescent agent, experiments were performed on tissue phantoms comprised of typical in-vivo tumor to normal tissue contrast ratios, ranging from 3.5:1 up to 10:1. It was found that tomographic imaging was able to recover interior inclusions with high contrast relative to the background; however, simple planar fluorescence imaging provided a superior contrast to noise ratio. Overall, FRI performed optimally when the object was located on or close to the surface and, perhaps most importantly, FDOT was able to recover specific depth information about the location of embedded regions. The results indicate that an optimal system for localizing embedded fluorescent regions should combine fluorescence reflectance imaging for high sensitivity and sub-surface tomography for depth detection, thereby allowing more accurate localization in all three directions within the tissue.

© 2008 Optical Society of America

1. Introduction

The propagation of visible and NIR light in biological tissue is dominated by absorption and scattering events which inherently impose geometrical and attenuation limitations that make clinical imaging at depth extremely challenging. Fluorescence imaging in a reflectance or remittance geometry, also known as fluorescence remission imaging (FRI), or planar imaging, is a common technique which is often used to create a topological map of the fluorescence intensity at the surface of the specimen. In recent years, this technology has been investigated for detecting lesions in animal models [4–7

4. J. S. Reynolds, T. L. Troy, R. H. Mayer, A. B. Thompson, D. J. Waters, K. K. Cornell, P. W. Snyder, and E. M. Sevick-Muraca, “Imaging of spontaneous canine mammary tumors using fluorescent contrast agents,” Photochem. Photobiol. 70, 87–94 (1999). [CrossRef] [PubMed]

], [8

8. B. W. Pogue, S. L. Gibbs, and B. Chen, “Fluorescence Imaging In Vivo: Raster Scanned Point-Source Imaging Provides More Accurate Quantification than Broad Beam Geometries,” Tech. Cancer Res. Treat. 3, 15–21 (2004).

], and as a surgical guidance probe for large animals [9

9. A. M. De Grand and J. V. Frangioni, “An operational near-infrared fluorescence imaging system prototype for large animal surgery,” Tech. Cancer Res. Treat. 2, 553–562 (2003).

] and humans [1

1. W. P. Stummer, Uwe, T. Meinel, O. D. Wiestler, F. Zanella, and H.-J. Reulen, “Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial” Lancet Oncol. 7, 392–401 (2006). [CrossRef]

, 2

2. A. Bogaards, A. Varma, S. P. Collens, A. Lin, A. Giles, V. Yang, J. M. Bilbao, L. D. Lilge, P. J. Muller, and B. C. Wilson, “Increased Brain Tumor Resection Using Fluorescence Image Guidance in a preclinical Model” Lasers Surg. Med. 35 (2004). [CrossRef] [PubMed]

]. Previously, Graves et al. [10

10. E. E. Graves, R. Weissleder, and V. Ntziachristos, “Fluorescence molecular imaging of small animal tumor models,” Curr. Molec. Med. 4, 419–430 (2004). [CrossRef]

] and Ntziachristos et al. [5

5. V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23, 313–320 (2005). [CrossRef] [PubMed]

] demonstrated the inability to image deep targets embedded in a highly scattering media using planar reflectance and transmission imaging techniques, respectively. In both studies, the planar techniques were contrasted with FDOT in a transmission geometry. The results indicated that imaging with transmission tomography provides superior localization and quantification of the underlying fluorochrome distributions. Unfortunately, this is not always a viable option because tissue has high absorbance and scattering properties in the visible/NIR frequency band, thereby making it difficult to maintain adequate SNR in dense or large tissues. As a result, transmission tomography is primarily limited to small soft tissue volumes, such as the breast.

In terms of clinical practice, sub-surface optical tomography may be the most promising because it allows the excitation and remission light to be delivered and collected from the same tissue surface. To date, sub-surface tomography has been used to non-invasively probe tissue volumes for breast cancer [11–13

11. V. Ntziachristos, A. G. Yodh, M. Schnall, and B. Chance, “Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement,” PNAS USA 97, 2767–2772 (2000). [CrossRef] [PubMed]

], brain function [14–16

14. M. Franceschini and D. A. Boas, “Noninvasive measurement of neuronal activity with near-infrared optical imaging,” NeuroImage 21, 372–386 (2004). [CrossRef] [PubMed]

], and small animals models[17–19

17. J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Met. 23, 911–924 (2003). [CrossRef]

]. Recently, Kepshire et al. [3

3. D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub–Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth,” Appl. Opt. 46, 1669–1678 (2007). [CrossRef] [PubMed]

] reported a non-linear relationship between recovered target fluorochrome and chromophore concentrations and depth in this geometry. The study also demonstrated that sub-surface tomography can localize embedded fluorescent lesions at depths up to 1 cm with a mean positional accuracy of approximately 1 mm. These results indicate potential for applications such as surgical guidance, where detection, rather than quantification of embedded lesions is more important. It should be noted that the experiments in [3

3. D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub–Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth,” Appl. Opt. 46, 1669–1678 (2007). [CrossRef] [PubMed]

] were idealized because they considered only infinite object contrast, and thus represent best case conditions for imaging sub-surface inclusions in this geometry.

In this work, the FRI and sub-surface FDOT techniques under investigation were optimized for imaging Protoporphyrin IX (Pp-IX) fluorescence. Pp-IX is preferentially produced in tumor cells through an increase in heme synthesis; its production in the brain has previously been shown to be significantly more abundant in glioma tumors than in the surrounding normal brain parenchyma, with contrasts of tumor to normal tissue near 6:1 being reported [20

20. S. A. Friesen, G. O. Hjortland, S. J. Madsen, H. Hirschberg, O. Engebraten, J. M. Nesland, and Q. Peng, “5-Aminolevulinic acid-based photodynamic detection and therapy of brain tumors,” Int. J. Oncol. 21, 577–582 (2002). [PubMed]

]. The lesion sizes to be detected are usually in the margins of normal tissue, following otherwise thorough tumor bulk resection. Thus the lesion sizes are not known exactly, but likely to be quite small, and of indeterminate position relative to the resection cavity surface. In this study, which evaluates the performance of FRI and FDOT for surgical guidance applications, experiments were performed using contrasts as low as 3.5:1, and sizes ranging from 2.5 mm up to 10 mm. Results were quantified and compared to assess the detectable contrast and the contrast to noise ratio of each modality.

2. Materials and methods

2.1 Instrumentation

2.1.1 Tomographic imaging

The non-contact fluorescence diffuse optical tomography (FDOT) imaging system illustrated in Fig. 1 (a) was used to collect fluorescence intensity signals in the ‘sub-surface reflectance’ geometry [21

21. B. W. Pogue, T. McBride, U. Osterberg, and K. Paulsen, “Comparison of imaging geometries for diffuse optical tomography of tissue,” Opt. Exp. 4, 270–286 (1999). [CrossRef]

]; that is, with the excitation and emission signals delivered and collected from the same side of the phantom surface. The instrumentation and data calibration components of this system have previously been described in detail by Kepshire et al. [3

3. D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub–Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth,” Appl. Opt. 46, 1669–1678 (2007). [CrossRef] [PubMed]

]. In the present work, a target containing an increased concentration of the Pp-IX fluorophore was submerged in a tissue-simulating liquid phantom, as shown in Fig. 2 (a), and positioned within the imaging plane. Non-contact excitation signals were delivered to the phantom surface using a 635nm collimated diode laser (Model CPS196, Thor Labs) and a pair of orthogonal galvanometers (Model 6220, Cambridge Technology). By dynamically adjusting the position of a single galvanometer, the laser source was made to raster scan along a single plane in the area under examination. Excitation and emission diffuse intensity signals were then separated by a 650nm long pass optical filter (Omega Optical) with a measured rejection ratio of 4 OD, directed through a lens of f 1.2, and collected using a temperature stabilized CCD camera (Sensicam QE, Cooke Corporation). For each source position, 15 ‘virtual detectors’ [22

22. J. Ripoll, R. B. Schulz, and V. Ntziachristos, “Free-space propagation of diffuse light: theory and experiments,” Phys. Rev. Lett. 91, 5 (2003). [CrossRef]

] were created by averaging groups of pixels in the camera’s 4cm (y-dimension) x 2.5mm (x-dimension) field-of-view (FOV) on the phantom surface, as shown in Fig. 1 (b). In building each dataset, 16 source positions were used to collect 240 measurements of the fluorescence diffusion along a single 1-D line in the x-y plane. This is illustrated in Fig. 1 (b). The data collected from the location of the active source was omitted to ensure the validity of the diffusion regime. The collected data is then used to reconstruct 2-D images of the subsurface distribution of fluorescence yield in the y-z plane, as shown schematically in Fig. 2 (b).

2.1.2 Surface imaging

2.2 Theory of tomographic image formation

FluoroFAST, a custom finite-element based software package [23

23. S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. Letters 10, 1–3 (2005).

], utilizes a nonlinear Newton-minimization approach [24

24. K. D. Paulsen and H. Jiang, “Spatially varying optical property reconstruction using a finite element diffusion equation approximation,” Med. Phys. 22, 691–701 (1995). [CrossRef] [PubMed]

] to obtain inverse solutions to the continuous wave diffusion Eqs [25]:

·Dx(r)Φx(r)μax(r)Φx(r)=Q0(r)
(1)
·Dm(r)·Φm(r)μam(r)Φm(r,ω)=Φm(r)ημaf(r)
(2)

Here, the excitation (x) and fluorescence emission (m) fields are governed by Eq. (1) and (2) which c represents the speed of light in the medium in each case. The diffusion coefficients are given by D x,m=1/[3(µax,m+µsx,m)] where µax and µam are the absorption coefficients and the reduced scattering coefficient, µs′. At the excitation wavelength, the isotropic source term at position r→ is given by Q 0 (r→). The excitation and emission fields at position r→ are then φx,m(r→). In (2), µaf is the fluorophore absorption, and η represents the fluorescence quantum yield. The inversion method utilizes a spatially variant modified-Tikhonov regularization parameter [26

26. B. W. Pogue, T. O. McBride, J. Prewitt, U. L. Osterberg, and K. D. Paulsen, “Spatially variant regularization improves diffuse optical tomography,” Appl. Opt. 38, 2950–2961 (1999). [CrossRef]

] to minimize the data-model misfit and iteratively recover the optical properties at each node in the finite-element model. Specifically, the optical property update Eq is given by:

μ=(T+λ(z)I)1T(ϕmeasϕcalc)
(3)

Here, µ is a generic symbol for the optical property of interest, λ(z) is a spatially variant regularization parameter; ϕimeas is the measured intensity data, ϕicalc is the computed model intensity data, I is the identity matrix, and ℑ is the Jacobian matrix. For all of the work presented in this study, a 2% change in the data model mismatch between iterations was used as the stopping criteria. A detailed description of the methodology and numerical procedures has been reported previously [3

3. D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub–Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth,” Appl. Opt. 46, 1669–1678 (2007). [CrossRef] [PubMed]

].

Fig. 1. The experimental FDOT setup is comprised of an excitation light source (D1), two orthogonal galvanometers (XY) for scanning the source position, a filter for attenuating the excitation light (F1), filters for separating the excitation and emission light (F2/F3), and a charge coupled device (CCD) camera for detection. This hardware set up, with the detector camera pointing upwards at a glass plate upon which phantoms and animals can be placed, is shown schematically in (a). The surface imaging experiments utilized a similar hardware configuration, but with a high-power broadbeam laser (D2). A schematic of the virtual detector scheme (b) used for tomography illustrates the intensity from a group of pixels (256) being averaged together to form a virtual detector.

2.3 Tissue simulating phantom experiments

The primary objective of the phantom experiments was to evaluate the ability to image biologically relevant contrasts over a range of depths using both FDOT and FRI. A liquid phantom shown in Fig. 2(a) was filled with water, 2% India ink (a simple black absorbing agent), 5% Tween-20 (a laboratory grade detergent) to create absorption coefficient, µa=0.0071 mm-1, and with 1% Intralipid™ to create reduced scattering coefficient, µs /=1.0 mm-1, to simulate the optical properties of tissue. Using Protoporphyrin IX (Pp-IX) as a fluorophore, the background Pp-IX concentration was fixed at 1 µg/mL, to create additional absorption coefficient due to the fluorophore of µaf=0.002 mm-1. This concentration was chosen to reflect known values of Pp-IX in vivo [27

27. C. Sheng, P. J. Hoopes, T. Hasan, and B. W. Pogue, “Photobleaching-based Dosimetry Predicts Deposited Dose in ALA-PpIX PDT of Rodent Esophagus,” Photochem. Photobiol. 83, 738–748 (2007). [CrossRef] [PubMed]

]. The concentration in the target was systematically increased to yield target to background contrasts of 3.5:1, 5:1, and 10:1. For each contrast, an 8mm diameter cylindrical target was submerged in the phantom and imaged at 5 different depths in the range 0–10 mm. The tomographic data was calibrated [3

3. D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub–Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth,” Appl. Opt. 46, 1669–1678 (2007). [CrossRef] [PubMed]

] and used to recover the subsurface spatial distribution of the fluorescent yield, ηµaf, which is the product of the fluorophore quantum yield and the fluorescent agent absorption coefficient. The optical properties at the excitation wavelength were assumed to be known. To generate surface images, filtered fluorescence data sets of the ROI under investigation were acquired before, ϕ i,j (homo_m), and after, ϕi,j (hetero_m), Pp-IX was added to the intralipid solution. Surface images ϕi,jfl were then generated by:

φfli,j=(φ(hetero_m)i,jφ(homo_m)i,j)
(3)

where i and j correspond to image pixels. By subtracting the background (ϕi,j (homo_m)) from the raw fluorescence dataset (ϕi,j (hetero_ m)), compensation for the background fluorescence and the excitation light not rejected by the filter was included.

Fig. 2. A photograph of the liquid phantom used in these experiments is shown in (a). All experiments were performed by submerging a cylindrical target in the liquid phantom shown here. Targets of varying diameter were filled with fluorescent contrast agents, positioned at varying depths and imaged using surface imaging and FDOT. In both cases imaging was performed from below; the experimental image dimensions are shown in schematic (b) for FRI (top) and FDOT (bottom).

2.4 Image analysis

Image contrast-to-noise analysis has been used to quantify performance in diffuse optical tomography [23

23. S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. Letters 10, 1–3 (2005).

, 28

28. X. Song, B. W. Pogue, S. Jiang, M. M. Doyley, H. Dehghani, T. D. Tosteson, and K. D. Paulsen, “Automated region detection based on the contrast-to-noise ratio in near-infrared tomography,” Appl. Opt. 43, 1053–1062 (2004). [CrossRef] [PubMed]

] and fluorescence diffuse optical tomography [23

23. S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. Letters 10, 1–3 (2005).

]. Though it avoids the subjective component introduced by a human observer, it remains an appropriate and effective method for quantitatively determine relatively accurate bounds of detectability for a given imaging system. Here, image contrast-to-noise ratio (CNR) was examined as a function of target depth from the surface for both fluorescence tomography and fluorescence surface imaging. In performing contrast-to-noise analysis the following Eq was selected to ensure proper weighting of the noise in the target and background regions [28

28. X. Song, B. W. Pogue, S. Jiang, M. M. Doyley, H. Dehghani, T. D. Tosteson, and K. D. Paulsen, “Automated region detection based on the contrast-to-noise ratio in near-infrared tomography,” Appl. Opt. 43, 1053–1062 (2004). [CrossRef] [PubMed]

]:

CNR=μafROIμafbkg(wROIσROI2wbkgσbkg2)
(4)

where µROIaf & µbkgaf are the mean node values in the target and background respectively, wROI & wbkg are weighting factors to account for the area of each ROI, and σ 2 ROI & σ 2 bkg are the calculated noise values in the target and background. Specifically, the noise weighting for the background and ROI were calculated as wbkg=Areabkg(AreaROI+Areabkg) and wROI=AreaROIAreaROI+Areabkg respectively. Image contrast, the relative difference between the fluorescence intensity in the target and the background, was then calculated according to:

Contrast=μafROIμafbkgμafbkg
(5)

To calculate the contrast and CNR in tomographic images, the image was interpolated onto a 10,000 node mesh. The mean signal and noise in the region of interest (ROI) was computed using the area inside a circle of 4mm radius from the reconstructed centroid location. The entire area outside of each target region was then used to compute the background signal and noise levels. In the surface imaging analysis, the pixel locations corresponding to the target and background were determined from the ‘True’ white light reflectance image shown in Fig. 6 (top). Examples of the region segmentation used in the ROI analysis are shown in Fig. 3 for tomographic (a) and surface (b) images, respectively.

Fig. 3. Examples of the imaged region segmentation used in the CNR and contrast calculations for tomographic (a) and surface (b) images. In the tomographic images, the target ROI was determined based on the position of the recovered centroid whereas the regions were fixed for the broadbeam analysis. The Tomographic image in (a) is a few into the medium, similar to an ultrasound B-scan, but the surface is located at the bottom of the image. The surface image in (b) shows the extended shape of the cylinder as viewed from the bottom surface.

3. Results

3.1 Subsurface fluorescence tomography

Fig. 4. A representative set of fluorescence image reconstructions in the remission-slab geometry, using a cylindrical region to be imaged. The images are shown as a function of depth into the medium (like an ultrasound image) with the surface at the bottom, and the round circular region being the cylinder shown cut through the middle (bottom). Images were reconstructed from experimental data collected for an 8 mm target submerged in a liquid phantom, when the target had 10:1, 5:1, and 3.5:1 fluorescent contrast with respect to the background. The true target locations are shown at the bottom of the Fig.
Fig. 5: Experimental fluorescence centroid results when the depth of an 8mm target was varied for contrasts of 10:1, 5:1, and 3.5:1. When contrasts of 5:1 and above were considered, mean positional error in the recovered centroid was determined to be 0.87 mm on average.

3.2 Fluorescence surface imaging

The same tissue simulating liquid phantom experiment was repeated using the surface imaging technique. Results for the case of an 8mm cylindrical target with a range of depths and target-to-background contrasts are shown in Fig. 6. For comparison, a white light image of the empty phantom positioned in the field-of-view under examination, denoted ‘True’, is shown at the top of this Fig. It is evident that FRI is able to recover the target quite well for all contrasts under examination when it is positioned within 2.5mm of the periphery, but this ability rapidly degrades with distance from the boundary; especially for realistic in-vivo contrasts below 10:1. When these low contrasts are considered empirically it is apparent that the fluorescence from an embedded target could possibly be misinterpreted as noise or intrinsic fluorophore heterogeneities. Moreover, surface imaging provides no means of quantifying the actual depth of the lesion.

Fig. 6. Experimental fluorescence broad beam imaging results when the depth and target-to-background contrast of an 8mm target was adjusted. It is clear that the ability to recover targets using this technique degrades rapidly with depth below the surface.

There is slight asymmetry in the illumination, due to the finite size of the light source, and the angle of illumination. This is true in almost all broad beam imaging systems, and while it can be seen in the images of Fig.s 3 and 6, especially in the last 10:1 contrast image, it does not significantly affect the conclusions of the experimental work. This asymmetry is more an artifact which must be dealt with in all systems, and can degrade detection of subsurface objects if not properly interpreted as such.

3.3 Image analysis

Image contrast (a) and contrast-to-noise (b) results for the tomographic and surface images are shown in Fig. 7 and Fig. 8, respectively. These Fig.s indicate that the contrast in FDOT is greater than planar imaging, but CNR is substantially better in surface imaging because of the low noise levels. This is not surprising since tomographic images are inherently noisy due to the ill-posed nature of the problem and the non-linear image reconstruction techniques that are involved. The maximum CNR in the tomographic images occurs around a depth of 2.5mm and not at the surface where the SNR is the highest. This may be due to a hypersensitivity near the imaging boundary [29

29. D. Kepshire, S. Gibbs, S. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub-surface Fluorescence Imaging of Protoporphyrin IX with B-Scan Mode Tomography,” Proc. SPIE 6139, In Press (2006). [CrossRef]

] or photon propagation that does not adhere to the diffusion approximation over short distances. Targets in the 2.5mm depth range yield the best reconstructed images not only in terms of contrast and CNR, but also in terms of centroid accuracy. Planar imaging measurements indicate an increasing trend in CNR around target depths of 7.5 to 10 mm in the surface imaging experiment. This may be attributed to the incident angle of the laser beam orientation relative to the phantom. In both cases, image contrast and CNR are governed by the depth of the target and the tissue/phantom optical properties because of the diffuse nature of the light propagation.

There is a slight increase in CNR values in Fig. 8(b) when the object is at deeper depths, and this was attributed to a decrease in spatial uniformity, or ‘noise’ rather than an true increase in contrast. This slight increase is an experimental artifact, due to the finite size of the source beam, rather than a true increase in CNR which is reliably reflecting detection of the object embedded below.

Fig. 7. Experimental fluorescence tomography recovered contrast (a) and recovered CNR (b) results when the depth of an 8mm target was varied for contrasts of 10:1, 5:1, and 3.5:1.
Fig. 8. Experimental recovered contrast (a) and recovered CNR (b) analysis for the set of fluorescence broad beam images depicted in Fig. 6.

4. Discussion

Although the inability to image embedded fluorescence targets at depth using planar imaging techniques has previously been demonstrated for transmission [5

5. V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23, 313–320 (2005). [CrossRef] [PubMed]

] and reflectance [10

10. E. E. Graves, R. Weissleder, and V. Ntziachristos, “Fluorescence molecular imaging of small animal tumor models,” Curr. Molec. Med. 4, 419–430 (2004). [CrossRef]

] geometries, the performance of fluorescence reflectance imaging (FRI) has yet to be quantitatively compared to sub-surface FDOT. Results from this study indicate that surface-based imaging is as beneficial as tomography when the tumor is or very close to the surface. However, as the target moves deeper into the medium the ability to easily detect the presence of an embedded target in the presence of endogenous background signal degrades rapidly (within millimeters) when surface imaging alone is used. This is readily seen in applications of subsurface imaging of tumors which express green fluorescence protein, where thin tissue layers over the tumor can yield detection of the tumor to be impossible. Although optimized surface imaging techniques have been demonstrated [30

30. A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of Fluorescent Tomography in thePresence of Heterogeneities: Study of theNormalized Born Ratio,” IEEE Trans. Med. Imag. 24, 1377–1386 (2005). [CrossRef]

, 31

31. S. C. Partridge, J. E. Gibbs, Y. Lu, L. J. Esserman, D. Tripathy, D. S. Wolverton, H. S. Rugo, E. S. Hwang, C. A. Ewing, and N. M. Hylton, “MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival,” Am. J.Roentgenol. 184, 1774–1781 (2005).

], results are not expected to improve to the point where this fundamental problem will be solved, because it is mostly limited by the basic physics of the signal recovery problem. Conversely, by compensating for the diffuse nature of light using the acquired boundary data and tomographic reconstruction techniques, the distribution of deep, highly scattered fluorochrome distributions can be more accurately estimated. In both FRI and FDOT, the ability to recover embedded fluorescence lesions within a turbid media is a function of target size, target-to-background contrast, and the optical properties of the medium. Although sub-surface tomography is likely less sensitive to small lesions (less than 4mm), it does have the ability to probe deeper than surface imaging and can provide specific depth information. Additionally, if prior information about the location of the region of interest below the surface could be provided by ultrasound or CT, it is feasible for the fluorescence to be quantified with reasonable accuracy. Studies of this apriori approach to diffuse tomography have been ongoing for sometime [32–34

32. R. L. Barbour, H. L. Graber, J. Chang, S. S. Barbour, P. C. Koo, and R. Aronson, “MRI-Guided Optical Tomography: Prospects and Computation for a New Imaging Method,” IEEE Comp. Sci. Eng. 2, 63–77 (1995). [CrossRef]

], with promising results shown for transmission imaging of breast cancers [35

35. Q. Zhu, N. Chen, and S. H. Kurtzman, “Imaging tumor angiogenesis by use of combined near-infrared diffusive light and ultrasound,” Opt. Lett. 28, 337–339 (2003). [CrossRef] [PubMed]

, 36

36. Q. Zhu, S. H. Kurtzma, P. Hegde, S. Tannenbaum, M. Kane, M. Huang, N. G. Chen, B. Jagjivan, and K. Zarfos, “Utilizing optical tomography with ultrasound localization to image heterogeneous hemoglobin distribution in large breast cancers,” Neoplasia7, 263–270 (2005). [CrossRef] [PubMed]

].

It should be noted that broad beam imaging can actually be created by a linear summation of the tomographic data collected during raster scanning of the source for all detection points. If the detection channel is summed for all source positions, then in the center of the imaging field, this becomes identical to the diffuse planar imaging case. This approximation breaks down near the edges of the field, where the source summation effect is then asymmetric, and not truly diffuse from all directions. Nonetheless, a nearly complete planar image and a tomographic image could both be developed using a single raster scanning system. Development of this type of combined approach to imaging both planar and tomographic images could be implemented.

It is possible that the limitations of the experiments and simulations here have led to inaccurate representation of how planar or sub-surface tomography could perform in vivo. For example, the diffusion modeling is a limited representation of the light distribution, however this will mainly affect the representation near the surface and within the first 3–4 mm of tissue, as shown by Farrell et al[38

38. T. J. Farrell, M. S. Patterson, and B. C. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties,” Med. Phys. 19, 879–888 (1992). [CrossRef] [PubMed]

]. Use of Monte Carlo or Radiation Transport models to simulate the forward data would undoubtedly lead to more accurate images, yet these cannot reliably be used in vivo, as the spatial map of the anisotropy coefficient and not the phase function cannot be readily known. So, while diffusion modeling limits the imaging system performance, it is likely the only realistic solution for tomography imaging of tissue.

5. Conclusions

Based on the results presented here, an optimal surgical guidance system would utilize both surface imaging and sub-surface tomography, particularly in the case of resection of residual disease, where the ROI is not necessarily located on the surface. Sensitivity to objects at depths down to about 1 cm can be achieved for anticipated contrast levels. Neurosurgical tumor resection under combined FRI/FDOT guidance has the potential to improve the number of full resections, as a FDOT based system should be capable of localizing residual tumors along the depth coordinate. The exact useful depth of which a neurosurgeon would use a fluorescence tomography system to remove tissue beyond which can be seen visually is not fully known, since the clinical study has not been done, however it seems as though additional resection beyond 1 cm would be unlikely. However the key value in FDOT versus existing FRI-guided resection is in finding deeper regions of tumor which may be obscured by overlying blood or tissue and the PPIX fluorescence is simply too weak to be viewed by the surgeon. In these cases the added value of tomography can only be determined if the system is used to determine if cases exist where additional tumors in the margin can be found. It is well known that local recurrence of the tumor is very high, indicating that remaining tumor areas are possibly present. The greatest challenges limiting FDOT from serving as a surgical guide is real-time operation and the expected complexity of the surface geometry. Recognition of the tradeoff in size, depth and contrast is an important issue which must be understood as a limiting factor in the sensitivity of these type of systems. Any decrease in size, contrast or depth will decrease the detected signal exponentially. Though this can be overcome somewhat using FDOT imaging, the response to objects at different depths is still non-linear.

Acknowledgments

This work has been funded by NCI grants RO1CA109558, PO1CA84203, U54CA105480 as well as the Norris Cotton Cancer Center Shared Resources.

References and links

1.

W. P. Stummer, Uwe, T. Meinel, O. D. Wiestler, F. Zanella, and H.-J. Reulen, “Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial” Lancet Oncol. 7, 392–401 (2006). [CrossRef]

2.

A. Bogaards, A. Varma, S. P. Collens, A. Lin, A. Giles, V. Yang, J. M. Bilbao, L. D. Lilge, P. J. Muller, and B. C. Wilson, “Increased Brain Tumor Resection Using Fluorescence Image Guidance in a preclinical Model” Lasers Surg. Med. 35 (2004). [CrossRef] [PubMed]

3.

D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub–Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth,” Appl. Opt. 46, 1669–1678 (2007). [CrossRef] [PubMed]

4.

J. S. Reynolds, T. L. Troy, R. H. Mayer, A. B. Thompson, D. J. Waters, K. K. Cornell, P. W. Snyder, and E. M. Sevick-Muraca, “Imaging of spontaneous canine mammary tumors using fluorescent contrast agents,” Photochem. Photobiol. 70, 87–94 (1999). [CrossRef] [PubMed]

5.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23, 313–320 (2005). [CrossRef] [PubMed]

6.

V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003). [PubMed]

7.

E. M. Sevick-Muraca, J. P. Houston, and M. Gurfinkel, “Fluorescence-enhanced, near infrared diagnostic imaging with contrast agents,” Curr. Op. Chem. Biol. 6, 642–650 (2002). [CrossRef]

8.

B. W. Pogue, S. L. Gibbs, and B. Chen, “Fluorescence Imaging In Vivo: Raster Scanned Point-Source Imaging Provides More Accurate Quantification than Broad Beam Geometries,” Tech. Cancer Res. Treat. 3, 15–21 (2004).

9.

A. M. De Grand and J. V. Frangioni, “An operational near-infrared fluorescence imaging system prototype for large animal surgery,” Tech. Cancer Res. Treat. 2, 553–562 (2003).

10.

E. E. Graves, R. Weissleder, and V. Ntziachristos, “Fluorescence molecular imaging of small animal tumor models,” Curr. Molec. Med. 4, 419–430 (2004). [CrossRef]

11.

V. Ntziachristos, A. G. Yodh, M. Schnall, and B. Chance, “Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement,” PNAS USA 97, 2767–2772 (2000). [CrossRef] [PubMed]

12.

B. W. Pogue, S. P. Poplack, T. O. McBride, W. A. Wells, O. K. S., U. L. Osterberg, and K. D. Paulsen, “Quantitative Hemoglobin Tomography with Diffuse Near-Infrared Spectroscopy: Pilot Results in the Breast,” Radiol. 218, 261–266 (2001).

13.

V. Ntziachristos, A. G. Yodh, M. D. Schnall, and B. Chance, “MRI-guided diffuse optical spectroscopy of malignant and benign breast lesions,” Neoplasia 4, 347–354 (2002). [CrossRef] [PubMed]

14.

M. Franceschini and D. A. Boas, “Noninvasive measurement of neuronal activity with near-infrared optical imaging,” NeuroImage 21, 372–386 (2004). [CrossRef] [PubMed]

15.

J. C. Hebden, A. Gibson, R. M. Yusof, N. Everdell, E. M. Hillman, D. T. Delpy, S. R. Arridge, T. Austin, J. H. Meek, and J. S. Wyatt, “Three-dimensional optical tomography of the premature infant brain,” Phys. Med. Biol. 47, 4155–4166 (2002). [CrossRef] [PubMed]

16.

A. Bluestone, G. Abdoulaev, C. Schmitz, R. Barbour, and A. Hielscher, “Three-dimensional optical tomography of hemodynamics in the human head,” Opt. Express 9, 272–286 (2001). [CrossRef] [PubMed]

17.

J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Met. 23, 911–924 (2003). [CrossRef]

18.

H. Xu, H. Dehghani, B. W. Pogue, R. F. Springett, K. D. Paulsen, and J. F. Dunn, “Near-infrared imaging in the small animal brain: optimization of fiber positions,” J. Biomed. Opt. 8, 102–110 (2003). [CrossRef] [PubMed]

19.

V. Ntziachristos, C. H. Tung, C. Bremer, and R. Weissleder, “Fluorescence molecular tomography resolves protease activity in vivo,” Nat. Med. 8, 757–760 (2002). [CrossRef] [PubMed]

20.

S. A. Friesen, G. O. Hjortland, S. J. Madsen, H. Hirschberg, O. Engebraten, J. M. Nesland, and Q. Peng, “5-Aminolevulinic acid-based photodynamic detection and therapy of brain tumors,” Int. J. Oncol. 21, 577–582 (2002). [PubMed]

21.

B. W. Pogue, T. McBride, U. Osterberg, and K. Paulsen, “Comparison of imaging geometries for diffuse optical tomography of tissue,” Opt. Exp. 4, 270–286 (1999). [CrossRef]

22.

J. Ripoll, R. B. Schulz, and V. Ntziachristos, “Free-space propagation of diffuse light: theory and experiments,” Phys. Rev. Lett. 91, 5 (2003). [CrossRef]

23.

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. Letters 10, 1–3 (2005).

24.

K. D. Paulsen and H. Jiang, “Spatially varying optical property reconstruction using a finite element diffusion equation approximation,” Med. Phys. 22, 691–701 (1995). [CrossRef] [PubMed]

25.

M. S. Patterson and B. W. Pogue, “Mathimatical model for time-resolved and frequency-domain fluorescence spectroscopy in biological tissues,” Appl. Opt. 33, 1963–1974 (1994). [CrossRef] [PubMed]

26.

B. W. Pogue, T. O. McBride, J. Prewitt, U. L. Osterberg, and K. D. Paulsen, “Spatially variant regularization improves diffuse optical tomography,” Appl. Opt. 38, 2950–2961 (1999). [CrossRef]

27.

C. Sheng, P. J. Hoopes, T. Hasan, and B. W. Pogue, “Photobleaching-based Dosimetry Predicts Deposited Dose in ALA-PpIX PDT of Rodent Esophagus,” Photochem. Photobiol. 83, 738–748 (2007). [CrossRef] [PubMed]

28.

X. Song, B. W. Pogue, S. Jiang, M. M. Doyley, H. Dehghani, T. D. Tosteson, and K. D. Paulsen, “Automated region detection based on the contrast-to-noise ratio in near-infrared tomography,” Appl. Opt. 43, 1053–1062 (2004). [CrossRef] [PubMed]

29.

D. Kepshire, S. Gibbs, S. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, “Sub-surface Fluorescence Imaging of Protoporphyrin IX with B-Scan Mode Tomography,” Proc. SPIE 6139, In Press (2006). [CrossRef]

30.

A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of Fluorescent Tomography in thePresence of Heterogeneities: Study of theNormalized Born Ratio,” IEEE Trans. Med. Imag. 24, 1377–1386 (2005). [CrossRef]

31.

S. C. Partridge, J. E. Gibbs, Y. Lu, L. J. Esserman, D. Tripathy, D. S. Wolverton, H. S. Rugo, E. S. Hwang, C. A. Ewing, and N. M. Hylton, “MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival,” Am. J.Roentgenol. 184, 1774–1781 (2005).

32.

R. L. Barbour, H. L. Graber, J. Chang, S. S. Barbour, P. C. Koo, and R. Aronson, “MRI-Guided Optical Tomography: Prospects and Computation for a New Imaging Method,” IEEE Comp. Sci. Eng. 2, 63–77 (1995). [CrossRef]

33.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack, and K. D. Paulsen, “Imaging Breast Adipose and Fibroglandular Tissue Molecular Signatures using Hybrid MRI-Guided Near-Infrared Spectral Tomography,” PNAS USA 103, 8828–8833 (2006). [CrossRef] [PubMed]

34.

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. Weaver, C. Kogel, and S. P. Poplack, “Combining near infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magentic reasonance structure,” J. Biomed. Opt. 10, 0515041–0515010 (2005). [CrossRef]

35.

Q. Zhu, N. Chen, and S. H. Kurtzman, “Imaging tumor angiogenesis by use of combined near-infrared diffusive light and ultrasound,” Opt. Lett. 28, 337–339 (2003). [CrossRef] [PubMed]

36.

Q. Zhu, S. H. Kurtzma, P. Hegde, S. Tannenbaum, M. Kane, M. Huang, N. G. Chen, B. Jagjivan, and K. Zarfos, “Utilizing optical tomography with ultrasound localization to image heterogeneous hemoglobin distribution in large breast cancers,” Neoplasia7, 263–270 (2005). [CrossRef] [PubMed]

37.

D. Piao, H. Xie, W. Zhang, J. Krasinski, G. Zhang, H. Dehghani, and B. W. Pogue, “Endoscopic, rapid near-infrared optical tomography,” Opt. Lett. 31, 2876–2878 (2006). [CrossRef] [PubMed]

38.

T. J. Farrell, M. S. Patterson, and B. C. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties,” Med. Phys. 19, 879–888 (1992). [CrossRef] [PubMed]

39.

B. W. Pogue, M. S. Patterson, H. Jiang, and K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995). [CrossRef] [PubMed]

40.

B. W. Pogue, S. Geimer, T. O. McBride, S. Jiang, U. L. Österberg, and K. D. Paulsen, “Three-dimensional Simulation of Near-Infrared Diffusion in Tissue: Boundary Condition and Geometry Analysis For Finite Element Image Reconstruction,” Appl. Opt. 40, 588–600 (2001). [CrossRef]

41.

H. Dehghani, B. W. Pogue, J. Shudong, B. Brooksby, and K. D. Paulsen, “Three-dimensional optical tomography: resolution in small-object imaging,” Appl. Opt. 42, 3117–3128 (2003). [CrossRef] [PubMed]

42.

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, 135–14(2003). [CrossRef] [PubMed]

OCIS Codes
(100.3190) Image processing : Inverse problems
(170.0110) Medical optics and biotechnology : Imaging systems
(170.0170) Medical optics and biotechnology : Medical optics and biotechnology
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.6960) Medical optics and biotechnology : Tomography

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: December 10, 2007
Revised Manuscript: March 5, 2008
Manuscript Accepted: March 14, 2008
Published: May 28, 2008

Virtual Issues
Vol. 3, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Dax Kepshire, Scott C. Davis, Hamid Dehghani, Keith D. Paulsen, and Brian W. Pogue, "Fluorescence tomography characterization for sub-surface imaging with protoporphyrin IX," Opt. Express 16, 8581-8593 (2008)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-16-12-8581


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References

  1. W. P. Stummer, U. T. Meinel, O. D. Wiestler, F. Zanella, and H.-J. Reulen, "Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial " Lancet Oncol. 7, 392-401 (2006). [CrossRef]
  2. A. Bogaards, A. Varma, S. P. Collens, A. Lin, A. Giles, V. Yang, J. M. Bilbao, L. D. Lilge, P. J. Muller, and B. C. Wilson, "Increased Brain Tumor Resection Using Fluorescence Image Guidance in a preclinical Model " Lasers Surg. Med. 35,181-190 (2004). [CrossRef] [PubMed]
  3. D. S. Kepshire, S. C. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, "Sub-Surface Diffuse Optical Tomography can Localize Absorber and Fluorescent Objects but Recovered Image Sensitivity is Non-Linear with Depth," Appl. Opt. 46, 1669-1678 (2007). [CrossRef] [PubMed]
  4. J. S. Reynolds, T. L. Troy, R. H. Mayer, A. B. Thompson, D. J. Waters, K. K. Cornell, P. W. Snyder, and E. M. Sevick-Muraca, "Imaging of spontaneous canine mammary tumors using fluorescent contrast agents," Photochem. Photobiol. 70, 87-94 (1999). [CrossRef] [PubMed]
  5. V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, "Looking and listening to light: the evolution of whole-body photonic imaging," Nat. Biotechnol. 23, 313-320 (2005). [CrossRef] [PubMed]
  6. V. Ntziachristos, C. Bremer, and R. Weissleder, "Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging," Eur. Radiol. 13, 195-208 (2003). [PubMed]
  7. E. M. Sevick-Muraca, J. P. Houston, and M. Gurfinkel, "Fluorescence-enhanced, near infrared diagnostic imaging with contrast agents," Curr. Op. Chem. Biol. 6, 642-650 (2002). [CrossRef]
  8. B. W. Pogue, S. L. Gibbs, and B. Chen, "Fluorescence Imaging In Vivo: Raster Scanned Point-Source Imaging Provides More Accurate Quantification than Broad Beam Geometries," Tech. Cancer Res. Treat. 3, 15-21 (2004).
  9. A. M. De Grand and J. V. Frangioni, "An operational near-infrared fluorescence imaging system prototype for large animal surgery," Tech. Cancer Res. Treat. 2, 553-562 (2003).
  10. E. E. Graves, R. Weissleder, and V. Ntziachristos, "Fluorescence molecular imaging of small animal tumor models," Curr. Molec. Med. 4, 419-430 (2004). [CrossRef]
  11. V. Ntziachristos, A. G. Yodh, M. Schnall, and B. Chance, "Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement," PNAS USA 97, 2767-2772 (2000). [CrossRef] [PubMed]
  12. B. W. Pogue, S. P. Poplack, T. O. McBride, W. A. Wells, O. K. S., U. L. Osterberg, and K. D. Paulsen, "Quantitative Hemoglobin Tomography with Diffuse Near-Infrared Spectroscopy: Pilot Results in the Breast," Radiology 218, 261-266 (2001).
  13. V. Ntziachristos, A. G. Yodh, M. D. Schnall, and B. Chance, "MRI-guided diffuse optical spectroscopy of malignant and benign breast lesions," Neoplasia 4, 347-354 (2002). [CrossRef] [PubMed]
  14. M. Franceschini and D. A. Boas, "Noninvasive measurement of neuronal activity with near-infrared optical imaging," NeuroImage 21, 372-386 (2004). [CrossRef] [PubMed]
  15. J. C. Hebden, A. Gibson, R. M. Yusof, N. Everdell, E. M. Hillman, D. T. Delpy, S. R. Arridge, T. Austin, J. H. Meek, and J. S. Wyatt, "Three-dimensional optical tomography of the premature infant brain," Phys. Med. Biol. 47, 4155-4166 (2002). [CrossRef] [PubMed]
  16. A. Bluestone, G. Abdoulaev, C. Schmitz, R. Barbour, and A. Hielscher, "Three-dimensional optical tomography of hemodynamics in the human head," Opt. Express 9, 272-286 (2001). [CrossRef] [PubMed]
  17. J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, "Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia," J. Cereb. Blood Flow Met. 23, 911-924 (2003). [CrossRef]
  18. H. Xu, H. Dehghani, B. W. Pogue, R. F. Springett, K. D. Paulsen, and J. F. Dunn, "Near-infrared imaging in the small animal brain: optimization of fiber positions," J. Biomed. Opt. 8, 102-110 (2003). [CrossRef] [PubMed]
  19. V. Ntziachristos, C. H. Tung, C. Bremer, and R. Weissleder, "Fluorescence molecular tomography resolves protease activity in vivo," Nat. Med. 8, 757-760 (2002). [CrossRef] [PubMed]
  20. S. A. Friesen, G. O. Hjortland, S. J. Madsen, H. Hirschberg, O. Engebraten, J. M. Nesland, and Q. Peng, "5-Aminolevulinic acid-based photodynamic detection and therapy of brain tumors," Int. J. Oncol. 21, 577-582 (2002). [PubMed]
  21. B. W. Pogue, T. McBride, U. Osterberg, and K. Paulsen, "Comparison of imaging geometries for diffuse optical tomography of tissue," Opt. Express 4, 270-286 (1999). [CrossRef]
  22. J. Ripoll, R. B. Schulz, and V. Ntziachristos, "Free-space propagation of diffuse light: theory and experiments," Phys. Rev. Lett. 91, 5 (2003). [CrossRef]
  23. S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, "Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction," J. Biomed. Opt. Lett. 10, 1-3 (2005).
  24. K. D. Paulsen and JiangH. , "Spatially varying optical property reconstruction using a finite element diffusion equation approximation," Med. Phys. 22, 691-701 (1995). [CrossRef] [PubMed]
  25. M. S. Patterson and B. W. Pogue, "Mathimatical model for time-resolved and frequency-domain fluorescence spectroscopy in biological tissues," Appl. Opt. 33, 1963-1974 (1994). [CrossRef] [PubMed]
  26. B. W. Pogue, T. O. McBride, J. Prewitt, U. L. Osterberg, and K. D. Paulsen, "Spatially variant regularization improves diffuse optical tomography," Appl. Opt. 38, 2950-2961 (1999). [CrossRef]
  27. C. Sheng, P. J. Hoopes, T. Hasan, and B. W. Pogue, "Photobleaching-based Dosimetry Predicts Deposited Dose in ALA-PpIX PDT of Rodent Esophagus," Photochem. Photobiol. 83, 738-748 (2007). [CrossRef] [PubMed]
  28. X. Song, B. W. Pogue, S. Jiang, M. M. Doyley, H. Dehghani, T. D. Tosteson, and K. D. Paulsen, "Automated region detection based on the contrast-to-noise ratio in near-infrared tomography," Appl. Opt. 43, 1053-1062 (2004). [CrossRef] [PubMed]
  29. D. Kepshire, S. Gibbs, S. Davis, H. Dehghani, K. D. Paulsen, and B. W. Pogue, "Sub-surface Fluorescence Imaging of Protoporphyrin IX with B-Scan Mode Tomography," Proc. SPIE 6139, In Press (2006). [CrossRef]
  30. A. Soubret, J. Ripoll, and V. Ntziachristos, "Accuracy of Fluorescent Tomography in thePresence of Heterogeneities: Study of theNormalized Born Ratio," IEEE Trans. Med. Imag. 24, 1377-1386 (2005). [CrossRef]
  31. S. C. Partridge, J. E. Gibbs, Y. Lu, L. J. Esserman, D. Tripathy, D. S. Wolverton, H. S. Rugo, E. S. Hwang, C. A. Ewing, and N. M. Hylton, "MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival," Am. J. Roentgenol. 184, 1774-1781 (2005).
  32. R. L. Barbour, H. L. Graber, J. Chang, S. S. Barbour, P. C. Koo, and R. Aronson, "MRI-Guided Optical Tomography: Prospects and Computation for a New Imaging Method," IEEE Comp. Sci. Eng. 2, 63-77 (1995). [CrossRef]
  33. B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack, and K. D. Paulsen, "Imaging Breast Adipose and Fibroglandular Tissue Molecular Signatures using Hybrid MRI-Guided Near-Infrared Spectral Tomography," PNAS USA 103, 8828-8833 (2006). [CrossRef] [PubMed]
  34. B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. Weaver, C. Kogel, and S. P. Poplack, "Combining near infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magentic reasonance structure," J. Biomed. Opt. 10, 051504-1-051504-10 (2005). [CrossRef]
  35. Q. Zhu, N. Chen, and S. H. Kurtzman, "Imaging tumor angiogenesis by use of combined near-infrared diffusive light and ultrasound," Opt. Lett. 28, 337-339 (2003). [CrossRef] [PubMed]
  36. Q. Zhu, S. H. Kurtzma, P. Hegde, S. Tannenbaum, M. Kane, M. Huang, N. G. Chen, B. Jagjivan, and K. Zarfos, "Utilizing optical tomography with ultrasound localization to image heterogeneous hemoglobin distribution in large breast cancers," Neoplasia 7, 263-270 (2005). [CrossRef] [PubMed]
  37. D. Piao, H. Xie, W. Zhang, J. Krasinski, G. Zhang, H. Dehghani, and B. W. Pogue, "Endoscopic, rapid near-infrared optical tomography," Opt. Lett. 31, 2876-2878 (2006). [CrossRef] [PubMed]
  38. T. J. Farrell, M. S. Patterson, and B. C. Wilson, "A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties," Med. Phys. 19, 879-888 (1992). [CrossRef] [PubMed]
  39. B. W. Pogue, M. S. Patterson, H. Jiang, and K. D. Paulsen, "Initial assessment of a simple system for frequency domain diffuse optical tomography," Phys. Med. Biol. 40, 1709-1729 (1995). [CrossRef] [PubMed]
  40. B. W. Pogue, S. Geimer, T. O. McBride, S. Jiang, U. L. ??sterberg, and K. D. Paulsen, "Three-dimensional Simulation of Near-Infrared Diffusion in Tissue: Boundary Condition and Geometry Analysis For Finite Element Image Reconstruction," Appl. Opt. 40, 588-600 (2001). [CrossRef]
  41. H. Dehghani, B. W. Pogue, J. Shudong, B. Brooksby, and K. D. Paulsen, "Three-dimensional optical tomography: resolution in small-object imaging," Appl. Opt. 42, 3117-3128 (2003). [CrossRef] [PubMed]
  42. 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, 135-145 (2003). [CrossRef] [PubMed]

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