Abstract

Most transitional cell tumorigenesis involves three stages of subcellular morphological changes: hyperplasia, dysplasia and neoplasia. Previous studies demonstrated that owing to its high spatial resolution and intermediate penetration depth, current OCT technology including endoscopic OCT could delineate the urothelium, submucosa and the upper muscular layers of the bladder wall. In this paper, we will discuss the sensitivity and limitations of OCT in diagnosing and staging bladder cancer. Based on histomorphometric evaluations of nuclear morphology, we modeled the resultant backscattering changes and the characteristic changes in OCT image contrast. In the theoretical modeling, we assumed that nuclei were the primary sources of scattering and were uniformly distributed in the uroepithelium, and compared with the results of the corresponding prior OCT measurements. According to our theoretical modeling, normal bladder shows a thin, uniform and low scattering urothelium, so does an inflammatory lesion except thickening in the submucosa. Compared with a normal bladder, a hyperplastic lesion exhibits a thickened, low scattering urothelium whereas a neoplastic lesion shows a thickened urothelium with increased backscattering. These results support our previous animal study that OCT has the potential to differentiate inflammation, hyperplasia, and neoplasia by quantifying the changes in urothelial thickening and backscattering. The results also suggest that OCT might not have the sensitivity to differentiate the subtle morphological changes between hyperplasia and dysplasia based on minor backscattering differences.

©2002 Optical Society of America

1. Introduction

Clinical statistics indicate that bladder cancer is the fifth most common cancer and the twelfth leading cause of cancer death in the US1. Bladder cancer is curable if detected and treated prior to invasion in the underlying bladder wall. Therefore, an earlier and more precise diagnosis of bladder cancer is critical to eradicating the disease, and understanding the pathological mutation of the disease on the microscopic level could lead to a better diagnosis because bladder carcinoma originates in the thin (20–200 μm) basal cell layer of the uroepithelium. Staging the spread or extent of invasion of cancerous urothelial cells into the underlying bladder wall is also important in helping the urologists to design the best treatment strategy. However, because of limitations of resolution, current detection methods, e.g., urine cytology, intravenous x-ray, MRI, and ultrasound fail to provide sufficient sensitivity or specificity2–6 in predicting the prognosis of early bladder cancers and staging their invasions. Cystoscopy, although commonly used in urological examination of superficial tumors (e.g., carcinomas in situ), is always followed by random biopsy for a conclusive diagnosis because of lacks of depth resolution. Accordingly, new approaches that can instantaneously provide cross-sectional images of bladder morphologies and their alternations (e.g., tumorigenesis) at close to cellular resolution would substantially enhance the diagnostic sensitivity and specificity of current cystoscopic approaches and result in significant therapeutic benefits.

Since its first introduction to imaging the eye in early 19903,4, optical coherence tomography (OCT) has found widespread applications in diagnosing diseases in various biological tissues7 such as human skin8, 9, tooth10, blood vessels, gastrointestinal tracts, respiratory tracts, and genitourinary tracts11, 12. In recent years, significant technological advances including polarization-sensitive OCT13, 14, spectral OCT15, ultra-high-resolution OCT16, and Doppler OCT17, have been made to improve image resolution and provide more specific diagnosis of physiological and functional information of biological tissue. Furthermore, because OCT is a fiber optically based light scanning imaging technique it can be integrated with endoscopic catheters to allow for noninvasive or minimally invasive in vivo imaging diagnosis of intraluminal tracts. Implementations of real-time endoscopic OCT using a rotary joint, a PZT transducer and a MEMS micromirror have been reported11, 18–20, showing a great promise to leverage the diagnostic capability of current endoscopic modalities.

Both ex vivo and in vivo OCT imaging of urinary bladder has been reported8, 16, 21; however, there has been no systematic study that correlates the micro morphology imaged by OCT with that provided by the corresponding histology. Such a study is needed to examine the utility and limitations of OCT in diagnosing and staging bladder cancers. In our previous paper5, we reported the first systematic OCT study of the course of bladder tumorigenesis in a well-characterized model in which Fisher rats were exposed to methyl-nitroso-urea (MNU), followed with cystoscope-like surface imaging, OCT and histology. The results suggested the potential of OCT for detection of bladder inflammatory lesions and early urothelial abnormalities by analyzing the variations in urothelial thickening and backscattering. However, further analytical study is needed to characterize the micro morphological changes at different stages of tumorigenesis (e.g., hyperplasia, dysplasia and neoplasia) with respect to the resultant changes in the OCT signature, i.e., backscattering, so that we can better predict the sensitivity and limitations of OCT in staging transitional cell tumors. In the present studies, we injected water, blood and intralipid into the submucosa of rat bladders to simulate different types of inflammatory lesions (e.g., edema, vasocongestion, inflammatory infiltrate) and characterized the resultant OCT contrast changes. We next modeled the optical characteristics and the resultant OCT signal changes based on the histomorphometric evaluations of subcellular morphological changes of urothelial cells at the three stages (i.e., hyperplasia, dysplasia and neoplasia) during tumorigenesis. We then compared the modeling results with the OCT measurements for those samples whose histological evaluations were previously harvested for optical modeling. This comparative study is important to characterize the OCT image contrast as a result of tumorigenesis and analyze the sensitivity of OCT for diagnosis and staging of bladder cancer.

2. Methods

2.1 High-Performance OCT Imaging System

The principle of OCT is similar to that of ultrasound imaging. A schematic diagram of our OCT system used to perform 2D cross-sectional images presented in this study is shown in Fig. 1, which is based on a fiber-optic Michelson interferometer. This technique performs high-resolution optical ranging or tomographic measurement by taking advantage of the short temporal coherence of a broadband light source. A high-brightness, broadband light source was used for illumination. Its pigtailed output power I is 13mW, central wavelength λ̄ is 1320nm, and half-maximum-full-width (HMFW) spectral bandwidth Δλ. is 77nm, thus yielding a coherence length LC of roughly 10μm2,23. The broadband light was split equally into the reference and the sample arms of the fiberoptic Michelson interferometer. Because a low-coherence source was used, the recombined light in the detection fiber only coherently interfered when the pathlengths in the sample and reference arms were matched to within the short coherence length, Lc≈10μm. Thus, moving the reference mirror permitted tomographic determination of the pathlength-resolved distribution of the interference modulation of light from the reflecting interfaces within the biological tissue placed in the sample arm. This offers a unique advantage of OCT over any other optical imaging modality in that it circumvents the need to scan a bulky microscopic lens to accomplish high-resolution imaging. The axial reflectance profile, i.e., the envelope of the interferometric signal was detected at high sensitivity by using optical heterodyne detection, locking in the Doppler frequency shifted signal (fD = 2v/λ, v is the speed for reference mirror scan). This signal was bandpass filtered and envelope demodulated prior to feeding to a PC for image display. Scanning the reference mirror at stable and high speed yields a constant Doppler frequency for optical heterodyne detection, and is thus critical to real-time high-fidelity OCT imaging. In our system, this was accomplished by using a double-pass grating-lens based optical delay line, a techniques used for fs laser Fourier-transform pulse shaping22. With proper settings of all the components, optical ranging up to 3mm was achieved at over 1kHz, thus allowing 2D OCT imaging at almost 4–8 frames/s.

In the sample arm, the light beam existing the fiber was collimated, deflected by a two-axis servo scanner, and then focused on the tissue specimen under examination with a microscopic objective (4x, NA0.1). A red laser diode (λ = 670 nm) along with a color camera was used to align the lateral position of the tissue specimen under examination; whereas the axial focusing of the incident beam was adjusted by the preset reference mirror position as precisely as less than 5 micrometers, taking advantage of the short source coherence length. A 2D cross-sectional image was produced by scanning the incident light beam across the tissue with a lateral servo scanner each time after the sequential reflectivity profile in the longitudinal direction was taken. The axial (Δz) and lateral (Δr) resolutions of the OCT system are determined by the coherence length and the microscopic lens used in the probe beam, respectively. The axial resolution (Δz) is 23,24

Δz=LC=(2ln2π)·(λ¯2Δλ)

The lateral or transverse resolution is determined by the focused spot size in analogy with conventional microscopy and is

Δr=2λ0πNA=4λ0fπϕ

where NA = ϕ/2f is the numerical aperture of the microscopic lens, f is the focal length of the microscopic lens and ϕ is the spot size of the light beams exiting the fiber optic collimator. For the OCT setup used in this study, they were roughly 10μm, respectively.

 figure: Fig. 1.

Fig. 1. Schematic diagram of the fiber optic OCT system. BBS: broadband light source; LD: aiming laser diode; PD: photo diode; CM: fiber-optic collimator. High-speed reference mirror scanning is grating-lens delay line.

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2.2 Optical modeling

To characterize OCT with histology for micro morphological changes induced by tumorigenesis, a controllable bladder tumor model was used by way of instillation of proper doses of a chemical carcinogen, MNU, into the bladder of Fisher rats. At different time points, e.g., at week 20, 24, 30, 36, the intact rat bladders were harvested, properly stretched and mounted onto ϕ10mm ring holders filled with 37°C modified Ringer's buffer solution. The apical or urothelial side of the bladder was faced up for cystoscope-like surface imaging, OCT, and H&E stained histology. The lateral positions of the OCT scan were precisely tattooed in Indian ink with the help of a red aiming laser and color CCD camera to guide later histological sectioning and light microscopy.

For the simulation study of different types of bladder inflammatory lesions, 3 groups of a total of 9 fresh normal rat bladder samples were mounted on ring holders to allow OCT imaging and the section scanned by OCT were pinned by a pair of 32 gauge needles as landmarks. Then, for group 1 and group 2, approximately 5 μl 0.9% saline and fresh whole blood previously taken from the same animal were injected into the submucosal layer of the sections previously scanned by OCT to simulate edema and severe edema with vasodilation, respectively. For group 3, the same amount of 10% intralipid was injected into submucosa to simulate accumulated inflammatory cells and other types of inflammatory infiltrate (e.g., necrosis, fibrosis) with increased scattering. Microinjection was performed under a stereo dissecting microscope using 32 gauge needles. A comparison between OCT images prior to and post microinjection allowed us to characterize OCT images for the changes of submucosal morphology and the resultant optical properties of bladder inflammatory lesions.

Clinical diagnosis of malignancy presently relies on high-resolution pathological analysis of subcellular morphology, e.g., increase of nuclear to cytoplastic ratio (NC ratio), and loss of polarity25. Because of technological limitations, current OCT (endoscopic OCT, in particular) does not provide sufficient resolution to image these subcellular changes in vivo. Nevertheless, the results of our previous study5, 21 suggested that the change in urothelial backscattering induced by nuclear morphology (e.g., increase in NC ratio, loss of polarity) detected by OCT might provide a potential method to differentiate between urothelial hyperplasia (precancer) and neoplasia (cancer).

Unfortunately, complicated wave optical effects (e.g. multiple scattering and speckle effects) in random medium such as biological tissue have hindered theoretical modeling of OCT; presently, there is no analytical or numerical model that can effectively analyze the OCT image contrast with respect to the histological evaluations. To tackle the challenge, we explored this complicated problem by combining analytical modeling with experimental measurements. For each tumor sample, multiple OCT scans were performed and followed by histological sections on the same areas. The H&E stained histological sections were photographed by a 4k×3k-pixel color digital CCD camera. Once hyperplastic, dysplastic and neoplastic lesions in these samples were identified, image processing was applied to the areas of the urothelium with pathological alternations. In the optical modeling of cellular scattering, we neglected the contributions of other scattering sources, e.g., mitochondria, cytoplastic contents and intercellular boundaries, and assumed that nuclei were the primary scattering sources of urothelial cells. Then, the urothelium was optically simplified as a 2D matrix consisting of scattering centers (nuclei) with refractive index of nN = 1.52 surrounded by a nonscattering solution with nC=1.38. The nuclei were assumed to be spherical and their sizes were counted to yield the mean nuclear size (dN) and ‘2D’ volumetric density ρs for scattering calculation. For mathematic simplicity, we further assumed all scattering centers were uniformly distributed, and the simplified urothelial model became a uniform 3D scattering matrix with equal particle size of dN and refractive index of nN. The 3D volumetric particle density ρv were calculated according to the relation,

ρv=1.33ρs32

Thus, based on the measured mean particle size dN and volumetric density ρs, the scattering characteristics, e.g., scattering coefficient μs, scattering anisotropy g or backscattering coefficient μb were calculated according to Mie-scattering algorithms. Based on our previous study, the interferometric signal detected by OCT was approximately given by 23,26–28

I˜d(Lr)=2IsIr·[R(Ls)C(Ls)]

where Ir is light intensity in the reference arm, IS is the light incident on the biological tissue. C(LS) =exp ⌊- 4(Ls/Lc)2⌋cos k̄Ls is low-coherence function, and R(LS) is the pathlength-resolved reflectance. Urothelium is a thin and relatively low-scattering tissue (except large papillary neoplastic lesions). Assuming that light scattering within the urothelial layer is homogeneous and within the single scattering regime; then, we could approximate the relations, LS = 2ncz (z is geometric depth), R(LS) ≅ μbe-μs2z. Thus, Eq.(4) can be simplified as

I˜d(z)=kI0μbeμSzΔLLCe4(ΔLLc)2cosk¯ΔL

The modular term relates to the speckle effect, i.e., the summation of the backscattered light fields from a local volume (ΔL≤Lc) containing a large collection of particles (nuclei), each of which has a probability of scattering light at different angles, polarization directions, and at different delays or phase shifts depending on the geometric distribution. Therefore, we can neglect the speckle effects and characterize the ‘speckle-free’ OCT image contrast changes on lesions during tumorigenesis by evaluating the first term √μb esz base on the measured histological distribution and the model analysis stated above.

3. Results

As previously reported5, 14, early acute chemical cystitis following MNU instillation involves injuries of urothelium, causing leakage of urine constituents through a defective bladder permeability barrier which begins a process of chronic inflammation of the underlying muscular layers and in turn causes fibrosis and urothelial malignancies. This protocol provides a controllable animal model to permit systematic study of bladder diseases such as inflammatory lesions, cystitis, and transitional cell tumorigenesis. For acute injury to the bladder, our previous OCT study revealed that based on its ability to identify urothelium, submucosa and muscles as well as the backscattering changes in these layers, OCT was able to detect urothelial denudation, submucosal edema, vasodilation, and infiltrate as evidenced by the corresponding histology5. However, because of complications in inflammatory changes which might involve changes in scattering, absorption or both and even the positioning of these changes, a conclusive diagnosis is sometimes difficult and an in-depth understanding is highly demanded. To study this problem, we simulated different types of inflammatory lesions by injecting saline, whole blood and intralipid into the submucosa of the rat bladders. The results are presented in Fig. 2.

 figure: Fig. 2.

Fig. 2. OCT images of a normal rabbit bladder (A) and rabbit bladder samples injected with saline (B), blood (C) and intralipid (D). U: urothelium, SM: submucosa, M: muscular layers.

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Panel A is the OCT image of a normal rat bladder. The three layers were clearly delineated based on their scattering difference and structural characteristics: the loosely stretched urothelium (U) is shown as a low scattering thin layer (~55μm thick), the submucosa (SM) is shown as a homogeneous high scattering layer (~190μm thick). Because of scattering induced light lose, the ~240μm thick muscular layers (M) is shown as less bright than SM but can be identified by the boundaries of bifurcated collagen bundles. Panel B is the OCT image following ~5μl 0.9% saline injection to simulate early edema with fluid buildup. Because of low viscosity, some saline might flow out of the bladder wall through the injection hole; however, minor swelling in SM is clearly visible (~280μm thick). In addition to the dark stripes filled with saline, the overall SM is slightly less scattering than that the normal SM in Panel A possibly resulted from saline perfusion. Like Panel A, the underlying bladder wall is clearly visible. Panel C is the OCT image following ~5μl whole bladder injection to simulate SM edema with vasodilation. The injection hole is not shown in the cross section; however, moderate swelling in SM is clearly visible (~350μm thick). Compared with Panels A and B, a substantial decrease of backscattering in SM is observed following microinjection of blood; and as a result, the underlying bladder morphology disappears. Panel D is the OCT image following ~5μl 10% intralipid injection to simulate accumulated inflammatory cells and other types of inflammatory lesions in SM resulting in increased scattering (e.g., necrosis fibrosis). Swelling in SM is detected (~310μm thick). Compared with Panel C, a localized increase of backscattering in the injected SM is observed following microinjection of intralipid. As a result, the underlying bladder morphology became less clear than Panels A and B.

Overall, the simulation results presented above support our previous study5 in that different types of inflammatory lesions can be identified by the scattering and absorption changes associated with the morphological changes. Because fluid buildup presented in early edema is less turbid or scattering, the overall bladder structure including the underlying muscular layers is clearly delineated by OCT as seen in Panel B. Blood at 1.3μm is of both high scattering and absorption (μs>20mm-1, g7gt;0.95, μa>0.5mm-1). Because of its highly forward scattering and high absorption nature, vasodilation may appear as less bright and absorptive, causing the underlying bladder structure invisible as has been seen in Panel C. Intralipid (μs≈10mm-1) is less scattering than whole blood but its scattering anisotropy is lower (g < 0.7). As a result, it shows a brighter SM in the injected area with missing underlying structure because of scattering loss (in the above layer). Inflammatory lesions with accumulated inflammatory cells and fibrosis, because both have higher μs and g, tend to show both higher backscattering and high attenuation in OCT images.

 figure: Fig. 3.

Fig. 3. Histologic pictures of normal, hyperplastic, dysplastic, and neoplastic urothelial cells.

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Figure 3 shows a group of H&E stained histological pictures of rat bladders at four critical stages of tumorigenesis: Panels A-D are normal, hyperplastic, dysplastic and neoplastic urothelia, respectively. By comparing the nuclear morphology, we can see that the hyperplastic lesion (B) depicts a similar distribution to that of normal urothelium (A) except the increase in cell layers or depth; whereas the dysplastic and neoplastic lesions (C and D) show not only increase in urothelial thickness but also increase in nuclear density and loss of polarity. In addition, Panel D also depicts heavy vascularization, which is typical for transitional cell cancers. Further image analysis of nuclear morphology in these pictures leads to the size of nuclei and their volumetric distributions as presented in Table 1.

Table 1 reveals that from normal to neoplastic urothelia, the nuclear size decreases slightly from 7.25 μm to 6.14 μm whereas the nuclear density increases drastically from 0.0011/μm to 0.0025/μm resulting in an increased nuclear to cytoplastic ratio. Using the measured data Table 1 we can calculate the corresponding backscattering and scattering coefficients μb and μs according Mie theory of scattering and the resultant OCT signals using Eq. (5).

Tables Icon

Table 1:. Histological evaluations of nuclear morphology

Figure 4 shows the simulated OCT signal changes according to Eq. (5). The result suggests that because of insignificant difference between curves A and B (≤ 15%), OCT may not have the sensitivity to differentiate the subtle micro morphological changes between hyperplasia and dysplasia. However, due to high NC ratio and the resultant backscattering increase OCT is able to differentiate neoplasia (cancer) from hyperplasia (precancer) with over 50% sensitivity. These results are in agreement with our previous studies: by comparing backscattering change, OCT was able to differentiate hyperplasia and neoplasia or precancer with TCC. Then, by quantifying urothelial thickening, OCT can differentiate precancer (hyperplasia and dysplasia) from normal urothelium.

 figure: Fig. 4.

Fig. 4. Calculated results of backscattering changes as a function of nuclear morphology (e.g., size, density depicted in Fig. 3).

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Figure 5 presents a pair of 2D OCT images of a hyperplastic lesion and a neoplastic lesion along with their histological pictures to validate the theoretical modeling. Two bladder samples were obtained at weeks 28 and 36 following MNU instillation. Although the bladder samples might have shrunk during histological processing, the overall micro morphology, e.g., U, SM and M provided by OCT correlates well with the histology. The thickened urothelia are indicated by U’. Normal urothelia under stretch were roughly 40–45μm thick. The hyperplastic lesion U’ in Panel A was 250μm or about 5 times thicker than the normal urothelium and the neoplastic lesion U’ in panel B was 900 μm or almost 20 times thicker the normal urothelium, both of which were substantially thickened. It is also demonstrated that the backscattering increases slightly from normal to hyperplasia (≤25%) but noticeably from hyperplasia to neoplasia (≥70%) in Fig. 5.

 figure: Fig. 5.

Fig. 5. Comparisons of hyperplastic and neoplastic rat bladders imaged by OCT with histology. U: normal urothelium, SM: submucosa, M: muscular layer, U’: diseased urothelium.

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To further quantify the backscattering changes, image processing was applied to extract the A-scans or vertical intensity profiles of OCT measurements both at the center of the urothelial lesions and in the surrounding normal urothelia for comparison. About 10 consequential A-scans were averaged laterally to reduce speckle noise in the calculation, and the results are shown in Fig. 6 in which the measured OCT signals related to backscattering are presented in arbitrary unit.

A comparison between these two groups of A-scans reveals that the measured OCT signal levels for normal urothelia were about 40 counts whereas those for hyperplastic and neoplastic lesions were 50 and 85 counts, respectively. In other words, compared with normal urothelium, there was less than 20% increase for the hyperplastic lesion and over 60% increase for neoplastic lesions, which is approximately in agreement to our theoretical modeling indicated in Eq. (5) and Fig. 4. The results support our previous OCT study and reveal that the normal bladder, inflammation, hyperplasia and neoplasia can be diagnosed by OCT. Inflammatory lesions can be differentiated based on submucosal swelling and the changes in optical turbidity (low scattering for fluid buildup and high scattering for accumulated inflammatory cells or fibrosis) and absorption (vasodilation). Urothelial hyperplasia (precancer) and neoplasia (TCC) can be differentiated from normal urothelium based on detection of the combined urothelial thickening and increase in backscattering.

 figure: Fig. 6.

Fig. 6. A-scans on the normal, hyperplastic and neoplastic regions acquired from the OCT images in Fig. 5. Consecutive A-scans were averaged to reduce speckle noises.

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4. Discussion

In this study, we simulated different types of bladder inflammatory lesions using normal rat bladders by microinjection of saline, whole bladder and intralipid. Based on this experimental model, we characterized the OCT signatures with respect to edema (e.g., fluid buildup), vasodilation and accumulated inflammatory cells, necrosis or cystitis. Based on the assumption that nuclei were the primary scattering contributors in the urothelial cells, we proposed a semi-analytical model to characterize the changes of optical properties (e.g., scattering and absorption) and OCT signatures due to micro morphological changes at different stages of tumorigenesis, and compared the simulation results with the corresponding OCT images. Both simulation results were in agreement with the experiments, suggesting that OCT has the potential to provide detailed morphological changes below the bladder surface to detect submucosal inflammation, urothelial precancer and early cancer by analyzing uroepithelial thickening and backscattering. The simulation results presented in Fig. 2 for bladder inflammatory lesions confirm the identifications or analyses of our previous study5, i.e., different types of submucosal inflammations could be discriminated by the variations of sunmucosal scattering and absorption. Early edema, modeled by saline injection, was characterized by an almost transparent, thickened submucosa with clear underlying bladder wall in OCT (panel B); severe edema with vasodilation, modeled by whole blood injection, was characterized by a dark, thickened submucosa with missing underlying bladder wall in OCT (panel C) because of the high-absorption and forward-scattering nature of blood at 1.3μm; inflammatory lesions (e.g., accumulated inflammatory cells, necrosis and fibrosis), simulated by intralipid injection, was characterized by a bright, thickened submucosa with missing underlying bladder wall in OCT (panel D). This is because with a beginning of chronic inflammation of the muscular layers, processes of sclerosis and fibrosis prevail in the connective tissue of the subepithelial and submucosal layers and the scattering from these structures increases. As the result, there is an increased brightness in these structures and in turn results in shadows below them as indicated by the arrow in Panel D. In all these lesions, a common feature was that the urothelium remained normal, i.e., no observation of abnormal thickening or obvious increase of backscattering (except focal denudations of urothelium).

Unlike other biomedical optical imaging techniques (i.e., time-resolved imaging), OCT provides microscopic images of turbid biological tissues. Current tissue optics derived within the framework of photon migration fails to provide useful description of OCT in high scattering regime. Furthermore, because of insufficient resolution for subcellular imaging and complications of other wave optical effects such as speckles, an effective modeling of OCT contrast and resolution in scattering tissue that can relate to tissue microscopic phenomena still remains a prevailing challenge in OCT study. To tackle this challenge, we proposed a semi-analytical model to analyze the relation of OCT contrast change as a result of the subcellular morphological changes induced by tumorigenesis. In this model, we assumed that nuclei were the predominant scattering sources of urothelial cells. Then, based on histomorphometric evaluations of nuclear morphology of urothelial cells (Fig. 2) at different stages of tumor development, we measured the mean scattering sizes and volumetric densities of these nuclei and the corresponding scattering characteristics (e.g., μs, g or μb) and calculated the resultant OCT contrast changes in the urothelium (Eq .(4)). The model analysis revealed that both nuclear density and size might vary with urothelial abnormalities, e.g., from normal urothelium to hyperplasia, dysplasia and neoplasia; nevertheless, our assessment showed that the changes in nuclear density was the major factor affecting the scattering performance in the urothelium and the measured OCT contrast. As has been demonstrated in Fig. 4, our optical modeling revealed a less than 15% increase in backscattering from normal urothelium to hyperplasia but an over 50% increase from precancer (hyperplasia, dysplasia) to TCC (neoplasia). Experiments presented in Fig. 5 and Fig. 6 showed that the measured OCT signals increased less than 25% at hyperplastic stage and over 70% at neoplastic stage. These model analyses confirmed the results of our previous animal study and demonstrated the capability of OCT to discriminate neoplastic urothelium from normal or precancerous urothelium based on their backscattering changes. Our model analysis also suggested that OCT might not have the sensitivity to resolve the subtle changes between hyperplasia and dysplasia based on the insignificant backscattering changes. The results demonstrated that because of resolution limitations, OCT was unable to image subcellular details. However, based on our study, this technology has the resolution to delineate cross-sectional bladder morphology (e.g., U, SM and upper MS) and has the sensitivity to image the local backscattering changes that reflect the subcellular and nuclear morphological changes induced by tumorigenesis. Therefore, by imaging the location of the lesions and urothelial thickening, OCT is potentially able to differentiate submucosal inflammations and urothelial abnormalities; by further analyzing the backscattering changes and location, OCT is potentially able to differentiating the types of inflammatory lesion as well as precancers (e.g., hyperplasia and dysplasia) and cancers (e.g., neoplasia) in the urothelium. Because the identification is based on local backscattering changes, OCT might fail to differentiate some complicated lesions. For instance, a lesion of severe edema with decreased SM scattering (e.g., vasodilation) may appear as a hyperplastic lesion; and an inflammatory lesion (e.g., necrosis or fibrosis) with denuded urothelium may appear as a neoplastic lesion in OCT image. Our experimental studies5, 21 suggest that the diagnostic sensitivity to differentiate these types of lesions can be greatly enhanced by comparing them with the morphology of the adjacent normal bladder wall.

Our theoretical model was semi-analytical and relied on histomorphometric evaluations as input, which is not readily available in most cases. It was mathematically simplified based on assumptions that nuclei were the primary scattering contributors, and were spherical, homogeneously distributed and in the single-scattering regime. Nevertheless, the results revealed that this model provided useful explanations to analyze the effects of subcellular morphological changes of tumorigenesis on the resultant OCT signature or contrast as a result of backscattering changes. Further improvement of the theoretical model will consider the heterogeneous nature of the urothelium (e.g., umbrella, intermediate and basal cell layers), loss of polarity (e.g., orientation of elongated nuclei), blood scattering and absorption due to microvascularization in the neoplastic urothelium to provide more precise analyses of these changes on OCT contrast.

The experiments presented in this study were ex vivo because rats, the only controllable animal model to generate transitional cell cancers by MNU installation, are too small to perform cystoscopy. However, we have developed and refined OCT endoscopes based on microelectromechanical system (MEMS) technology19, 21 that have demonstrated image resolution and contrast close to the bench-top setup used in this study. We are in the process to launch in vivo study for the detection of TCCs and carcinomas in situ based on the expertise we have accumulated in the current systematical studies using this controllable animal tumor model. On the other hand, recent technological advances in ultra-broadband fs laser technology have permitted subcellular OCT at 1–3μm resolution16, 24, thus immensely enhancing the sensitivity and specificity of OCT for the diagnoses of bladder cancers and other types of epithelial cancers. These OCT technological developments and clinical applications have demonstrated the potential of endoscopic OCT for noninvasive or minimally invasive and instantaneous ‘optical biopsy’ or ‘optically guided biopsy’ to diagnose and stage early-stage cancers. Recent studies have showed that 5-aminolevulinic acid (5-ALA) induced fluorescence endoscopy has demonstrated the potential to enhance the diagnosis of malignant/dysplastic bladder lesions30–31; and our recent ex vivo study suggests that a new systoscopic suite guiding OCT with a 5-ALA fluorescence scope can drastically enhance the diagnostic specificity for precancerous lesions and reduce the time needed to scan the entire bladder (submitted to J. of Urology).

5. Conclusions

In summary, OCT has demonstrated the capability to delineate urinary bladder morphology, e.g., urothelium, submucosa and upper muscular layers as evidenced by the corresponding histology. Our model study confirms that OCT may be able to identify different types of submucosal inflammatory lesions based on the difference of scattering and absorption patterns in these lesions. Based on histomorphometric evaluation of nuclear morphology, the proposed semi-analytical model predicts a minor difference (<25%) in backscattering between normal and hyperplastic urothelia and a substantial increase (70%) between hyperplastic and neoplastic urothelia, which is approximately in agreement with OCT measurements. These results demonstrate that OCT has the potential to differentiate normal bladder, inflammatory lesion, urothelial precancer and TCC by analyzing the location of the lesion, urothelial thickening and backscattering. Overall, OCT has the potential to serve as useful biopsy-guiding tool for noninvasive or minimally invasive diagnosis of bladder tumors to reduce random, negative biopsies.

Acknowledgements

This research was supported in part by grants from the Whitaker Foundation (Y. P.) and the National Institutes of Health (Y. P., M, Z.). Special thanks to Susan Meyers and R. Ramage in the Department of Medicine, University of Pittsburgh for handling the animal studies. Please address future correspondence to Y. -T. Pan by email at yingtian.pan@sunysb.edu.

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8. A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994). [CrossRef]  

9. J. Welzel, “Optical coherence tomography in dermatology: a review,” Skin Research and Technology 7, 1–9 (2001). [CrossRef]   [PubMed]  

10. B. W. Colston, U. S. Sathyam, L. B. DaSilva, M. J. Everett, P. Stroeve, and L. L. Otis, “Dental OCT,” Opt. Express 3, 230–238 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-230. [CrossRef]   [PubMed]  

11. G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997). [CrossRef]   [PubMed]  

12. A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, R. V. Kuranov, N. D. Gladkova, N. M. Shakhova, L. B. Snopova, A. V. Shakhov, I. A. Kuznetzova, A. N. Denisenko, V. V. Pochinko, Yu. P. Chumakov, and O. S. Streltzova, “In vivo endoscopic OCT imaging of precancer and cancer states of human mucosa,” Opt. Express 1, 432–440 (1997), http://epubs.osa.org/oearchive/source/2788.htm. [CrossRef]   [PubMed]  

13. Johannes F. de Boer, Shyam M. Srinivas, Arash Malekafzali, Zhongping Chen, and J. Stuart Nelson, “Imaging thermally damaged tissue by polarization sensitive optical coherence tomography,” Opt. Express 3, 212–218 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-212. [CrossRef]   [PubMed]  

14. Gang Yao and Lihong Wang, “Propagation of polarized light in turbid media: simulated animation sequences”, Opt. Express 7, 198–203 (2000), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-7-5-198. [CrossRef]   [PubMed]  

15. M. Wojtkowski, A. Kowalczyk, R. Leitgeb, and A. F. Fercher, “Full range complex spectral optical coherence tomography technique in eye imaging,” Opt. Lett. 27, 1415–1417 (2002). [CrossRef]  

16. W. Drexler, U. Morgner, F. X. Kartner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography”, Opt. Lett. 24, 1221–1223 (1999). [CrossRef]  

17. Volker Westphal, Siavash Yazdanfar, Andrew M. Rollins, and Joseph A. Izatt “Real-time, high velocity-resolution color Doppler optical coherence tomography”, Opt. Lett. 27, 34–36 (2002). [CrossRef]  

18. F. I. Feldchtein, V. M. Gelikonov, G. V. Gelikonov, A. M. Sergeev, N. D. Gladkova, A. V. Shakhov, N. M. Shakhova, L. B. Snopova, A. B. Terent’eva, E. V. Zagainova, Y. P. Chumakov, and I. A. Kuznetzova, “Endoscopic applications of optical coherence tomography,” Opt. Express 3, 257–270 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-257. [CrossRef]   [PubMed]  

19. Y. Pan, H. Xie, and G. K. Fedder, “Endoscopic optical coherence tomography based on a microelectromechanical mirror,” Opt. Lett. 26, 1966–1968 (2001). [CrossRef]  

20. T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

21. Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.

22. Andrew Rollins, Joseph Izatt, Manish Kulkarni, Siavash Yazdanfar, and Rujchai Ung-arunyawee, “In vivo video rate optical coherence tomography”, Opt. Express 3, 219–229 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-219. [CrossRef]   [PubMed]  

23. Yingtian Pan, Reginald Birngruber, Jurgen Rosperich, and Ralf Engelhardt, “Low-coherence optical tomography in turbid tissue: theoretical analysis,” Appl. Opt. 34, 6564–6574 (1995). [CrossRef]   [PubMed]  

24. M. E. Brezinski and J. G. Fujimoto, “Optical Coherence Tomography: High-Resolution Imaging in Nontransparent Tissue,” IEEE J. Sel. Top. Quantum Electron. 5, 1185–1192 (1999). [CrossRef]  

25. William M. Murphy, Bruce Beckwith, and George M. FarrowTumors of the kidney, bladder and related urinary structures, Chapter 2 (Armed Forces Institute of Pathology, Washington, 1994).

26. J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993). [CrossRef]  

27. J. M. Schmitt and A. Knuttel, “Model of optical coherence tomography of heterogeneous tissue,” J. Opt. Soc. Am. A 14, 1231–1242 (1997). [CrossRef]  

28. M. Kowalevicz, T. Ko, I. Hartl, J. G. Fujimoto, M. Pollnau, and R. P. Salathe, “Ultrahigh resolution optical coherence tomography using a superluminescent light source,” Opt. Express 10, 349–353, 2002. [CrossRef]   [PubMed]  

29. Gang Yao and Lihong V Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44, 2307–2320 (1999). [CrossRef]   [PubMed]  

30. M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996). [CrossRef]  

31. F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999). [CrossRef]   [PubMed]  

References

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  1. http://www.cancernews.com/category.asp?cat=28&aid=235, “Diagnosis and Treatment of Bladder Cancer.”
  2. J. M. Schmitt, “Optical Coherence Tomography (OCT): A Review,” IEEE J. Sel. Top. Quantum Electron. 5, 1205–1215 (1999).
    [Crossref]
  3. A. F. Fercher, K. Mengedoht, and W. Werner, “Eye length measurement by interferometry with partially coherent light,” Opt. Lett. 13, 186–188 (1988).
    [Crossref] [PubMed]
  4. D. Huang, E. A. Swanson, and C. P. Lin, et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
    [Crossref] [PubMed]
  5. Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
    [Crossref]
  6. E. A. Swanson, J. A. Izatt, M. R. Hee, D. Huang, C. A. Puliafito, J. S. Schuman, and J. G. Fujimoto, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864 (1993).
    [Crossref] [PubMed]
  7. C. A. Puliafito, M. R. Hee, J. S. Schuman, and J. G. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK, Thorofare, NJ, 1996).
  8. A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
    [Crossref]
  9. J. Welzel, “Optical coherence tomography in dermatology: a review,” Skin Research and Technology 7, 1–9 (2001).
    [Crossref] [PubMed]
  10. B. W. Colston, U. S. Sathyam, L. B. DaSilva, M. J. Everett, P. Stroeve, and L. L. Otis, “Dental OCT,” Opt. Express 3, 230–238 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-230.
    [Crossref] [PubMed]
  11. G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
    [Crossref] [PubMed]
  12. A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, R. V. Kuranov, N. D. Gladkova, N. M. Shakhova, L. B. Snopova, A. V. Shakhov, I. A. Kuznetzova, A. N. Denisenko, V. V. Pochinko, Yu. P. Chumakov, and O. S. Streltzova, “In vivo endoscopic OCT imaging of precancer and cancer states of human mucosa,” Opt. Express 1, 432–440 (1997), http://epubs.osa.org/oearchive/source/2788.htm.
    [Crossref] [PubMed]
  13. Johannes F. de Boer, Shyam M. Srinivas, Arash Malekafzali, Zhongping Chen, and J. Stuart Nelson, “Imaging thermally damaged tissue by polarization sensitive optical coherence tomography,” Opt. Express 3, 212–218 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-212.
    [Crossref] [PubMed]
  14. Gang Yao and Lihong Wang, “Propagation of polarized light in turbid media: simulated animation sequences”, Opt. Express 7, 198–203 (2000), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-7-5-198.
    [Crossref] [PubMed]
  15. M. Wojtkowski, A. Kowalczyk, R. Leitgeb, and A. F. Fercher, “Full range complex spectral optical coherence tomography technique in eye imaging,” Opt. Lett. 27, 1415–1417 (2002).
    [Crossref]
  16. W. Drexler, U. Morgner, F. X. Kartner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography”, Opt. Lett. 24, 1221–1223 (1999).
    [Crossref]
  17. Volker Westphal, Siavash Yazdanfar, Andrew M. Rollins, and Joseph A. Izatt “Real-time, high velocity-resolution color Doppler optical coherence tomography”, Opt. Lett. 27, 34–36 (2002).
    [Crossref]
  18. F. I. Feldchtein, V. M. Gelikonov, G. V. Gelikonov, A. M. Sergeev, N. D. Gladkova, A. V. Shakhov, N. M. Shakhova, L. B. Snopova, A. B. Terent’eva, E. V. Zagainova, Y. P. Chumakov, and I. A. Kuznetzova, “Endoscopic applications of optical coherence tomography,” Opt. Express 3, 257–270 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-257.
    [Crossref] [PubMed]
  19. Y. Pan, H. Xie, and G. K. Fedder, “Endoscopic optical coherence tomography based on a microelectromechanical mirror,” Opt. Lett. 26, 1966–1968 (2001).
    [Crossref]
  20. T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).
  21. Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.
  22. Andrew Rollins, Joseph Izatt, Manish Kulkarni, Siavash Yazdanfar, and Rujchai Ung-arunyawee, “In vivo video rate optical coherence tomography”, Opt. Express 3, 219–229 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-219.
    [Crossref] [PubMed]
  23. Yingtian Pan, Reginald Birngruber, Jurgen Rosperich, and Ralf Engelhardt, “Low-coherence optical tomography in turbid tissue: theoretical analysis,” Appl. Opt. 34, 6564–6574 (1995).
    [Crossref] [PubMed]
  24. M. E. Brezinski and J. G. Fujimoto, “Optical Coherence Tomography: High-Resolution Imaging in Nontransparent Tissue,” IEEE J. Sel. Top. Quantum Electron. 5, 1185–1192 (1999).
    [Crossref]
  25. William M. Murphy, Bruce Beckwith, and George M. FarrowTumors of the kidney, bladder and related urinary structures, Chapter 2 (Armed Forces Institute of Pathology, Washington, 1994).
  26. J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
    [Crossref]
  27. J. M. Schmitt and A. Knuttel, “Model of optical coherence tomography of heterogeneous tissue,” J. Opt. Soc. Am. A 14, 1231–1242 (1997).
    [Crossref]
  28. M. Kowalevicz, T. Ko, I. Hartl, J. G. Fujimoto, M. Pollnau, and R. P. Salathe, “Ultrahigh resolution optical coherence tomography using a superluminescent light source,” Opt. Express 10, 349–353, 2002.
    [Crossref] [PubMed]
  29. Gang Yao and Lihong V Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44, 2307–2320 (1999).
    [Crossref] [PubMed]
  30. M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
    [Crossref]
  31. F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
    [Crossref] [PubMed]

2002 (3)

2001 (3)

Y. Pan, H. Xie, and G. K. Fedder, “Endoscopic optical coherence tomography based on a microelectromechanical mirror,” Opt. Lett. 26, 1966–1968 (2001).
[Crossref]

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

J. Welzel, “Optical coherence tomography in dermatology: a review,” Skin Research and Technology 7, 1–9 (2001).
[Crossref] [PubMed]

2000 (1)

1999 (5)

W. Drexler, U. Morgner, F. X. Kartner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography”, Opt. Lett. 24, 1221–1223 (1999).
[Crossref]

J. M. Schmitt, “Optical Coherence Tomography (OCT): A Review,” IEEE J. Sel. Top. Quantum Electron. 5, 1205–1215 (1999).
[Crossref]

M. E. Brezinski and J. G. Fujimoto, “Optical Coherence Tomography: High-Resolution Imaging in Nontransparent Tissue,” IEEE J. Sel. Top. Quantum Electron. 5, 1185–1192 (1999).
[Crossref]

Gang Yao and Lihong V Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44, 2307–2320 (1999).
[Crossref] [PubMed]

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

1998 (4)

1997 (3)

1996 (1)

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

1995 (1)

1994 (1)

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

1993 (2)

E. A. Swanson, J. A. Izatt, M. R. Hee, D. Huang, C. A. Puliafito, J. S. Schuman, and J. G. Fujimoto, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864 (1993).
[Crossref] [PubMed]

J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
[Crossref]

1991 (1)

D. Huang, E. A. Swanson, and C. P. Lin, et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

1988 (1)

Baumgartner, R.

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

Bckhause, M A

J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
[Crossref]

Beckwith, Bruce

William M. Murphy, Bruce Beckwith, and George M. FarrowTumors of the kidney, bladder and related urinary structures, Chapter 2 (Armed Forces Institute of Pathology, Washington, 1994).

Birngruber, Reginald

Boppart, S. A.

W. Drexler, U. Morgner, F. X. Kartner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography”, Opt. Lett. 24, 1221–1223 (1999).
[Crossref]

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

Bouma, B. E.

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

Brezinski, M. E.

M. E. Brezinski and J. G. Fujimoto, “Optical Coherence Tomography: High-Resolution Imaging in Nontransparent Tissue,” IEEE J. Sel. Top. Quantum Electron. 5, 1185–1192 (1999).
[Crossref]

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

Chen, Zhongping

Chumakov, Y. P.

Chumakov, Yu. P.

Colston, B. W.

DaSilva, L. B.

de Boer, Johannes F.

Denisenko, A. N.

Deutsch, T. F.

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

Dmitriev, G. I.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Drexler, W.

Engelhardt, Ralf

Enquist, H.

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

Everett, M. J.

Farrow, George M.

William M. Murphy, Bruce Beckwith, and George M. FarrowTumors of the kidney, bladder and related urinary structures, Chapter 2 (Armed Forces Institute of Pathology, Washington, 1994).

Fedder, G. K.

Y. Pan, H. Xie, and G. K. Fedder, “Endoscopic optical coherence tomography based on a microelectromechanical mirror,” Opt. Lett. 26, 1966–1968 (2001).
[Crossref]

T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

Feldchtein, F. I.

Fercher, A. F.

Frakas, D. L.

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Fujimoto, J. G.

M. Kowalevicz, T. Ko, I. Hartl, J. G. Fujimoto, M. Pollnau, and R. P. Salathe, “Ultrahigh resolution optical coherence tomography using a superluminescent light source,” Opt. Express 10, 349–353, 2002.
[Crossref] [PubMed]

M. E. Brezinski and J. G. Fujimoto, “Optical Coherence Tomography: High-Resolution Imaging in Nontransparent Tissue,” IEEE J. Sel. Top. Quantum Electron. 5, 1185–1192 (1999).
[Crossref]

W. Drexler, U. Morgner, F. X. Kartner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography”, Opt. Lett. 24, 1221–1223 (1999).
[Crossref]

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

E. A. Swanson, J. A. Izatt, M. R. Hee, D. Huang, C. A. Puliafito, J. S. Schuman, and J. G. Fujimoto, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864 (1993).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, J. S. Schuman, and J. G. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK, Thorofare, NJ, 1996).

Gelikonov, G. V.

Gelikonov, V. M.

Gladkova, N. D.

Hartl, I.

Hee, M. R.

E. A. Swanson, J. A. Izatt, M. R. Hee, D. Huang, C. A. Puliafito, J. S. Schuman, and J. G. Fujimoto, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864 (1993).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, J. S. Schuman, and J. G. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK, Thorofare, NJ, 1996).

Hofstaedter, F.

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

Hofstetter, A.

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

Huang, D.

Ippen, E. P.

Izatt, J. A.

Izatt, Joseph

Izatt, Joseph A.

Kartner, F. X.

Knuechel, R.

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

Knuttel, A

J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
[Crossref]

Knuttel, A.

Ko, T.

Koenig, F.

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

Kowalczyk, A.

Kowalevicz, M.

Kriegmair, M.

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

Kulkarni, Manish

Kuranov, R. V.

Kuznetzova, I. A.

Larne, R.

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

Lavelle, J. P.

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Leitgeb, R.

Li, X. D.

Li, Zhigang

Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.

Lin, C. P.

D. Huang, E. A. Swanson, and C. P. Lin, et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Malekafzali, Arash

McGovern, F. J.

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

Mengedoht, K.

Meyers, S.

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Morgner, U.

Murphy, William M.

William M. Murphy, Bruce Beckwith, and George M. FarrowTumors of the kidney, bladder and related urinary structures, Chapter 2 (Armed Forces Institute of Pathology, Washington, 1994).

Nikulin, N. K.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Otis, L. L.

Pan, Y.

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Y. Pan, H. Xie, and G. K. Fedder, “Endoscopic optical coherence tomography based on a microelectromechanical mirror,” Opt. Lett. 26, 1966–1968 (2001).
[Crossref]

T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

Pan, Yingtian

Yingtian Pan, Reginald Birngruber, Jurgen Rosperich, and Ralf Engelhardt, “Low-coherence optical tomography in turbid tissue: theoretical analysis,” Appl. Opt. 34, 6564–6574 (1995).
[Crossref] [PubMed]

Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.

Petrova, G. A.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Pirtskhalaishvili, G.

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Pitris, C.

W. Drexler, U. Morgner, F. X. Kartner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography”, Opt. Lett. 24, 1221–1223 (1999).
[Crossref]

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

Pochinko, V. V.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, R. V. Kuranov, N. D. Gladkova, N. M. Shakhova, L. B. Snopova, A. V. Shakhov, I. A. Kuznetzova, A. N. Denisenko, V. V. Pochinko, Yu. P. Chumakov, and O. S. Streltzova, “In vivo endoscopic OCT imaging of precancer and cancer states of human mucosa,” Opt. Express 1, 432–440 (1997), http://epubs.osa.org/oearchive/source/2788.htm.
[Crossref] [PubMed]

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Pollnau, M.

Pravdenko, K. I.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Puliafito, C. A.

E. A. Swanson, J. A. Izatt, M. R. Hee, D. Huang, C. A. Puliafito, J. S. Schuman, and J. G. Fujimoto, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864 (1993).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, J. S. Schuman, and J. G. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK, Thorofare, NJ, 1996).

Rollins, Andrew

Rollins, Andrew M.

Rosperich, Jurgen

Salathe, R. P.

Sathyam, U. S.

Schmitt, J M

J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
[Crossref]

Schmitt, J. M.

J. M. Schmitt, “Optical Coherence Tomography (OCT): A Review,” IEEE J. Sel. Top. Quantum Electron. 5, 1205–1215 (1999).
[Crossref]

J. M. Schmitt and A. Knuttel, “Model of optical coherence tomography of heterogeneous tissue,” J. Opt. Soc. Am. A 14, 1231–1242 (1997).
[Crossref]

Schomacker, K. T.

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

Schuman, J. S.

E. A. Swanson, J. A. Izatt, M. R. Hee, D. Huang, C. A. Puliafito, J. S. Schuman, and J. G. Fujimoto, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864 (1993).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, J. S. Schuman, and J. G. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK, Thorofare, NJ, 1996).

Sergeev, A. M.

Shabanov, D. V.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Shakhov, A. V.

Shakhova, N. M.

Snopova, L. B.

Southern, J. F.

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

Srinivas, Shyam M.

Stepp, H.

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

Streltzova, O. S.

Stroeve, P.

Stuart Nelson, J.

Swanson, E. A.

Tearney, G. J.

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

Terent’eva, A. B.

Ung-arunyawee, Rujchai

V Wang, Lihong

Gang Yao and Lihong V Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44, 2307–2320 (1999).
[Crossref] [PubMed]

Vazina, I. R.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Wang, Lihong

Welzel, J.

J. Welzel, “Optical coherence tomography in dermatology: a review,” Skin Research and Technology 7, 1–9 (2001).
[Crossref] [PubMed]

Werner, W.

Westphal, Volker

Wojtkowski, M.

Xie, H.

Y. Pan, H. Xie, and G. K. Fedder, “Endoscopic optical coherence tomography based on a microelectromechanical mirror,” Opt. Lett. 26, 1966–1968 (2001).
[Crossref]

T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

Xie, T.

T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

Xie, Tuqiang

Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.

Yadlowsky, M

J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
[Crossref]

Yao, Gang

Gang Yao and Lihong Wang, “Propagation of polarized light in turbid media: simulated animation sequences”, Opt. Express 7, 198–203 (2000), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-7-5-198.
[Crossref] [PubMed]

Gang Yao and Lihong V Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44, 2307–2320 (1999).
[Crossref] [PubMed]

Yazdanfar, Siavash

Zagainova, E. V.

Zeidel, M.

T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

Zeidel, M. L.

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Zeidel, Mark L.

Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.

Zhegalov, V. A.

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Appl. Opt. (1)

BJU International (1)

F. Koenig, F. J. McGovern, R. Larne, H. Enquist, K. T. Schomacker, and T. F. Deutsch, “Diagnosis of bladder carcinoma using protoporphyrin IX fluorescence induced by 5-aminolaevulinic acid,” BJU International 83, 129–135 (1999).
[Crossref] [PubMed]

IEEE J. Sel. Top. Quantum Electron. (2)

M. E. Brezinski and J. G. Fujimoto, “Optical Coherence Tomography: High-Resolution Imaging in Nontransparent Tissue,” IEEE J. Sel. Top. Quantum Electron. 5, 1185–1192 (1999).
[Crossref]

J. M. Schmitt, “Optical Coherence Tomography (OCT): A Review,” IEEE J. Sel. Top. Quantum Electron. 5, 1205–1215 (1999).
[Crossref]

J. of Urology (1)

M. Kriegmair, R. Baumgartner, R. Knuechel, H. Stepp, F. Hofstaedter, and A. Hofstetter, “Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence,” J. of Urology 155, 105–110 (1996).
[Crossref]

J. Opt. Soc. Am. A (1)

Med. Phys. (1)

Y. Pan, J. P. Lavelle, S. Meyers, G. Pirtskhalaishvili, M. L. Zeidel, and D. L. Frakas, “Detection of tumorigenesis in rat bladders with optical coherence tomography,” Med. Phys. 28, 2432–2440 (2001).
[Crossref]

Opt. Express (7)

B. W. Colston, U. S. Sathyam, L. B. DaSilva, M. J. Everett, P. Stroeve, and L. L. Otis, “Dental OCT,” Opt. Express 3, 230–238 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-230.
[Crossref] [PubMed]

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, R. V. Kuranov, N. D. Gladkova, N. M. Shakhova, L. B. Snopova, A. V. Shakhov, I. A. Kuznetzova, A. N. Denisenko, V. V. Pochinko, Yu. P. Chumakov, and O. S. Streltzova, “In vivo endoscopic OCT imaging of precancer and cancer states of human mucosa,” Opt. Express 1, 432–440 (1997), http://epubs.osa.org/oearchive/source/2788.htm.
[Crossref] [PubMed]

Johannes F. de Boer, Shyam M. Srinivas, Arash Malekafzali, Zhongping Chen, and J. Stuart Nelson, “Imaging thermally damaged tissue by polarization sensitive optical coherence tomography,” Opt. Express 3, 212–218 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-212.
[Crossref] [PubMed]

Gang Yao and Lihong Wang, “Propagation of polarized light in turbid media: simulated animation sequences”, Opt. Express 7, 198–203 (2000), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-7-5-198.
[Crossref] [PubMed]

F. I. Feldchtein, V. M. Gelikonov, G. V. Gelikonov, A. M. Sergeev, N. D. Gladkova, A. V. Shakhov, N. M. Shakhova, L. B. Snopova, A. B. Terent’eva, E. V. Zagainova, Y. P. Chumakov, and I. A. Kuznetzova, “Endoscopic applications of optical coherence tomography,” Opt. Express 3, 257–270 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-257.
[Crossref] [PubMed]

M. Kowalevicz, T. Ko, I. Hartl, J. G. Fujimoto, M. Pollnau, and R. P. Salathe, “Ultrahigh resolution optical coherence tomography using a superluminescent light source,” Opt. Express 10, 349–353, 2002.
[Crossref] [PubMed]

Andrew Rollins, Joseph Izatt, Manish Kulkarni, Siavash Yazdanfar, and Rujchai Ung-arunyawee, “In vivo video rate optical coherence tomography”, Opt. Express 3, 219–229 (1998), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-3-6-219.
[Crossref] [PubMed]

Opt. Lett. (6)

Phys. Med. Biol. (2)

J M Schmitt, A Knuttel, M Yadlowsky, and M A Bckhause, “Optical-coherence tomography of a dense tissue: statistics of attention of backscattering,” Phys. Med. Biol. 39,1705–1720 (1993).
[Crossref]

Gang Yao and Lihong V Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44, 2307–2320 (1999).
[Crossref] [PubMed]

Proc. SPIE (2)

A. M. Sergeev, V. M. Gelikonov, G. V. Gelikonov, F. I. Feldchtein, K. I. Pravdenko, D. V. Shabanov, N. D. Gladkova, V. V. Pochinko, V. A. Zhegalov, G. I. Dmitriev, I. R. Vazina, G. A. Petrova, and N. K. Nikulin, “In vivo optical coherence tomography of human skin microstructure,” Proc. SPIE 2328, 144–150 (1994).
[Crossref]

Tuqiang Xie, Zhigang Li, Mark L. Zeidel, and Yingtian Pan, “Optical imaging diagnostics of bladder tissue with optical coherence tomography”, Proc. SPIE 4609, in print.

Science (2)

G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, “In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography,” Science 276, 2037–2039 (1997).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, and C. P. Lin, et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Skin Research and Technology (1)

J. Welzel, “Optical coherence tomography in dermatology: a review,” Skin Research and Technology 7, 1–9 (2001).
[Crossref] [PubMed]

Other (4)

C. A. Puliafito, M. R. Hee, J. S. Schuman, and J. G. Fujimoto, Optical Coherence Tomography of Ocular Diseases (SLACK, Thorofare, NJ, 1996).

http://www.cancernews.com/category.asp?cat=28&aid=235, “Diagnosis and Treatment of Bladder Cancer.”

T. Xie, H. Xie, G. K. Fedder, M. Zeidel, and Y. Pan, “Endoscopic Optical Coherence Tomography with a Micromachined Mirror,” 2nd Annual International IEEE-EMBS, Madison, Wisconsin, USA, May 2–4, 208–211 (2002).

William M. Murphy, Bruce Beckwith, and George M. FarrowTumors of the kidney, bladder and related urinary structures, Chapter 2 (Armed Forces Institute of Pathology, Washington, 1994).

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Figures (6)

Fig. 1.
Fig. 1. Schematic diagram of the fiber optic OCT system. BBS: broadband light source; LD: aiming laser diode; PD: photo diode; CM: fiber-optic collimator. High-speed reference mirror scanning is grating-lens delay line.
Fig. 2.
Fig. 2. OCT images of a normal rabbit bladder (A) and rabbit bladder samples injected with saline (B), blood (C) and intralipid (D). U: urothelium, SM: submucosa, M: muscular layers.
Fig. 3.
Fig. 3. Histologic pictures of normal, hyperplastic, dysplastic, and neoplastic urothelial cells.
Fig. 4.
Fig. 4. Calculated results of backscattering changes as a function of nuclear morphology (e.g., size, density depicted in Fig. 3).
Fig. 5.
Fig. 5. Comparisons of hyperplastic and neoplastic rat bladders imaged by OCT with histology. U: normal urothelium, SM: submucosa, M: muscular layer, U’: diseased urothelium.
Fig. 6.
Fig. 6. A-scans on the normal, hyperplastic and neoplastic regions acquired from the OCT images in Fig. 5. Consecutive A-scans were averaged to reduce speckle noises.

Tables (1)

Tables Icon

Table 1: Histological evaluations of nuclear morphology

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

Δ z = L C = ( 2 ln 2 π ) · ( λ ¯ 2 Δ λ )
Δ r = 2 λ 0 πNA = 4 λ 0 f πϕ
ρ v = 1.33 ρ s 3 2
I ˜ d ( L r ) = 2 I s I r · [ R ( L s ) C ( L s ) ]
I ˜ d ( z ) = k I 0 μ b e μ S z Δ L L C e 4 ( Δ L L c ) 2 cos k ¯ Δ L

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