Developments in optical imaging and microscopy have been a major component of the recent gains in understanding and diagnosing human biology and disease. The development of new microscopy methods combined with the merging of optics and genetics are dramatically changing the pace of discovery. However, assessing human biology at the cellular and sub-cellular scale often requires the removal, processing, and expert evaluation of tissue. These requirements delay the availability of diagnostic information for medical decisions, while at the same time limit our ability to study function as it occurs in unperturbed cells. Native contrast optical techniques are often based on light scattering and absorption in cells and tissue, and can be used to gain quantitative physiological and structural information. This information can often yield rapid and highly reproducible diagnostic information that is independent of staining interactions, making these approaches ideally suited for clinical translation. Recent improvements in computation and data processing have opened the opportunity for developing high throughput imaging and automated diagnostic methods. The combination of native contrast diagnostic methods with automated diagnostic algorithms has been a major focus of my research.

Computational Modeling of Light Propagation

Photon scattering
Diagram of a single photon path into and out of a tissue-like scattering medium. Monte Carlo simulations make us of random number generators to simulate many such paths and obtain a light distribution based on photon statistics. Analytical approximations, are typically compared against these simulations for accuracy. Figure adapted from ref. 3.
Predicting how light propagates in tissue can be accomplished with either analytical approximations or computational modeling. Using these predictions to translate a surface measurement into knowledge about the internal structure is a challenge that is often referred to as "the inverse problem". This inverse problem is particularly interesting for the case of small source-detector separations and shallow light penetration depths due to the possibility of obtaining sub-cellular information about the epithelium, the tissue layer of interest in the vast majority of cancers.

            Developing and validating inverse models for superficial light scattering has been a significant focus of my research.1,2 One major development was our publication of the first closed-form analytical solution that extends the diffusion theory to the area near the light source, a previously unsolved problem in photon transport through turbid media which had been grappled with for decades. Our solution to this problem involved a correction to the diffusion approximation, resulting in unprecedented accuracy at small source-detector separation. This result was published in Nature Communications.3


Technologies for Cancer Diagnosis

Fiber optic spectroscopy of pancreatic cysts through the needle during an endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) procedure. EUS-FNA is typically used to collect the internal contents of pancreatic cysts that are under evaluation for malignancy. Unfortunately, the collected fluid material is very often insufficient to obtain a diagnosis. By Inserting a small fiber optic spectroscopy probe through the needle and collecting measurements from the internal cyst wall, a much more accurate assessment of the cyst is possible. Figure adapted from ref. 17.
The discovery of Low-coherence Enhanced Backscattering Spectroscopy (LEBS)4 resulted in important technologies for characterizing tissue and selecting epithelial layer scattering.5 My interests focused on the development of LEBS into a cancer screening technology, which we evaluated for pancreatic cancer6 and colon cancer detection.7 Notably, cancer detection was accomplished with measurements that were made from tissue that was outside the boundary of the cancer tumor, thus observing the "field effect" of carcinogenesis.8,9 Field carcinogenesis is an organ-wide alteration in the tissue and cellular structure that accompanies the development of cancer. The "field effect" is correlated with the presence of genetic mutations, epigenetic alterations, and alterations in protein expression and its mechanisms are now an active area of research. In our studies, we use the field effect to identifying individuals at risk of having pre-cancerous and cancerous lesions. Our group was the first to demonstrate the detection of the field effect using optical methods, opening a path for cancer screening with LEBS and other optical spectroscopy modalities.6-10 We also discovered that the epithelial hemoglobin concentration is increased in the field effect.11-13 a phenomenon called Early Increased Blood Supply (EIBS). This probe design and analysis method has now been used in clinical trials as an in vivo screening tool for colon cancer,14 pancreatic cancer,15 and lung cancer.16

            Recently, we were able to apply optical spectroscopy technologies to the challenging area of pancreatic cyst diagnostics. We developed a needle-based fiber optic spectroscopy probe compatible with endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) diagnostic clinical procedures achieving accurate and minimally invasive pancreatic cancer diagnosis, with the paper describing that achievement recently published in Nature Biomedical Engineering.17


Microscopic Spectroscopy for Native Contrast Imaging

cancer cells
CLASS imaging of esophageal adenocarcinoma cell lines and tumor samples. (A) Confocal reflectance images from cell lines (left) and tumor samples (right) with the indicated degree of differentiation. (B) Size contributions color-mapped over the grayscale reflectance images of the cells (left), and the ratio of small (<0.84µm) to intermediate (1.5 to 4 µm) size particles mapped over the grayscale of the tumor sample image (right). Cells were cultured and measured in vitro. Well differentiated and moderately differentiated tumor samples were measured after deparaffinization.
Perhaps the most widely used method for of obtaining information about a disease state is through microscopic imaging. My research has included extending microscopic imaging by combining it with spectroscopy. One such method, called Confocal Light Absorption and Scattering Spectroscopic (CLASS) microscopy18 accomplishes this without the need for any staining, resulting in native contrast images. Initially, I worked on further developing this type of microscope to be able to extract rare fetal nucleated cells from maternal blood for the purpose of fetal genetic testing. I developed a high throughput version of the microscope that used both light scattering spectroscopy and fluorescence to identify rare fetal cells. The use of automated scanning and classification algorithms allowed for the successful identification of rare fetal cells from the maternal blood supply. The developed system is also compatible with laser-capture microdissection, used to isolate single fetal nucleated cells for genetic analysis.

The new CLASS microscope design, introduced in IEEE J Sel Top Quantum Electron19, allows acquiring high resolution spectroscopy images with high NA oil-immersion optics. This improved system, was used to study the growth and development of cancer cells during cell culture. Combining high resolution imaging with spectroscopy allowed us to investigate the sub-cellular alterations that accompany varying differentiation levels in esophageal cancer. The figure on the right shows an example illustrating the capabilities of this spectroscopic microscope, which allows spectral analysis of the reflectance signal at each pixel, resulting in a size distribution of structures within the focal volume. This can be used to obtain structure-based contrast as shown for esophageal cancer cell lines in vitro (left) or fixed tissue from esophageal cancer patients (right). The spectral information is useful for improving size-dependent contrast, as shown in the figure, obtaining the size distributions of sub-cellular structures, and determining the alterations in these size distributions in disease. This analysis also makes super-resolution size discrimination possible.18



1.     V. Turzhitsky, A. Radosevich, J. D. Rogers, A. Taflove, V. Backman, "A predictive model of backscattering at subdiffusion length scales," Biomed Opt Express, 1, 1034-1046 (2010).

2.     V. Turzhitsky, J.D. Rogers, N. Mutyal, H. K. Roy, V. Backman, “Characterization of light transport in scattering media at sub-diffusion length scales with low-coherence enhanced backscattering”, IEEE J Sel Top Quantum Electron, 16(3), 619-626 (2010).

3.     E. Vitkin, V. Turzhitsky, L. Qiu, L. Y. Guo, I. Itzkan, E. B. Hanlon, L. T. Perelman, "Phase function corrected diffusion approximation for light transport in turbid media," Nature Comm,  Dec 13 2:587

4.     Y.L. Kim, Y. Liu, V. Turzhitsky, H.K. Roy, R.K. Wali, and V. Backman, “Coherent backscattering spectroscopy,” Opt Lett, 29(16), 1906-1908 (2004).

5.     Y. L. Kim, Y. Liu, V. Turzhitsky, R. Wali, H. Roy, and V. Backman, "Depth-resolved low-coherence enhanced backscattering", Opt Lett, 30(7), 741-743 (2005).

6.     V. Turzhitsky, Y. Liu, N. Hasabou, M. Goldberg, H. K. Roy, Vadim Backman, Randall Brand, “Investigating population risk factors of pancreatic cancer by evaluation of optical markers in the duodenal mucosa”, Dis Markers, 25(6):313-21 (2008).

7.     H. K. Roy, V. Turzhitsky, Y. Kim, M. J. Goldberg, P. Watson, J. D. Rogers, A. J. Gomes, A. Kromine, R. E. Brand, M. Jameel, N. Hasabou, P. Pradhan, V. Backman, "Association between rectal optical signatures and colonic neoplasia: potential applications for screening", Cancer Res, 69(10), 4476-4483 (2009).

8.     Y. L. Kim, V. Turzhitsky, Y. Liu, H. K. Roy, R. K. Wali, H. Subramanian, P. Pradhan, V. Backman, “Low-coherence enhanced backscattering (LEBS): review of principles and applications for colon cancer screening”, J Biomed Opt, 11(4), 041125-1-10 (2006).

9.     H. K. Roy, Y. L. Kim, Y. Liu, R. K. Wali, M. J. Goldberg, V. Turhitsky, J. Horwitz, V. Backman, “Risk-stratification of colon carcinogenesis through enhanced backscattering (EBS) spectroscopy: analysis of the uninvolved colonic mucosa”, Clin Cancer Res, 19(3), 961-968 (2006).

10.   Y. Liu, R. E. Brand, V. Turzhitsky, Y. L. Kim, H. K. Roy, N. Hasabou, C. Sturgis, D. Shah, C. Hall, V. Backman, “Optical markers in duodenal mucosa predict the presence of pancreatic cancer”, Clin Cancer Res, 13(15), 4392-4399 (2007).

11.   H. K. Roy, A. Gomes, V. Turzhitsky, M. J. Goldberg, J. Rogers, S. Ruderman, Y. L. Kim, A. Kromine, R. E. Brand, M. Jameel, N. Hasabou, V. Backman, “Spectroscopic microvascular blood detection from the endoscopically normal colonic mucosa: biomarker for neoplasia risk”, Gastroenterology, 135(4), 1069-1078 (2008).

12.   V. Turzhitsky, A. J. Gomes, Y. L. Kim, Y. Liu, A. Kromine, J. D. Rogers, M. Jameel, H. K. Roy, V. Backman, “Measuring mucosal blood supply in vivo with a polarization gating probe”, Appl Opt, 47(32), 6046-6057 (2008).

13.   A. J. Gomes, H. K. Roy, V. Turzhitsky, Y. Kim, J. D. Rogers, S. Ruderman, V. Stoyneva, M. J. Goldberg, L.K. Bianchi, E. Yen, A. Kromine, M. Jameel, V. Backman, “Rectal mucosal microvascular blood supply increase is associated with colonic neoplasia”, Clin Cancer Res, 15(9)3110-7 (2009).

14. A.J. Radosevich, N.N. Mutyal, A. Eshein, T.Q. Nguyen, B. Gould, J. Rogers, M. Goldberg, L. Bianchi, V. Konda, D. Rex, E. Yen, J. Van Dam, H.K. Roy, V. Backman, “Rectal optical markers for in vivo risk stratification of premalignant colorectal lesions,”, Clinical Cancer Research, 21(19), 4337-55 (2015). PMC4592390

15.   N.N. Mutyal, A.J. Radosevich, S. Bajaj, V. Konda, U.D. Siddiqui, I. Waxman, M.J. Goldberg, J.D. Rogers, B. Gould, A. Eshein, S. Upadhye, A. Koons, M. Gonzalez, H.K. Roy, V. Backman, “In-vivo risk analysis of pancreatic cancer through optical characterization of duodenal mucosa”, Pancreas, 44(5), 735-741 (2015).

16.   A.J. Radosevich, N.N. Mutyal, J.D. Rogers, B. Gould, T.A. Hensing, D. Ray, V. Backman, H.K.Roy, “Buccal spectral markers for lung cancer risk stratification”, PLoS One, 9(10): e110157 (2014).


17.   L. Zhang, D. K. Pleskow, V. Turzhitsky, E. U. Yee, T. M. Berzin, M. Sawhney, S. Shinagare, E. Vitkin, Y. Zakharov, U. Khan, F. Wang, J. D. Goldsmith, S. Goldberg, R. Chuttani, I. Itzkan, L. Qiu, and L. T. Perelman, "Light scattering spectroscopy identifies malignant potential of pancreatic cysts during endoscopy," Nature Biomed Eng,  1:40(2017).

18.   I. Itzkan, L. Qiu, H. Fang, M. M. Zaman, E. Vitkin, I. C. Ghiran, S. Salahuddin, M. Modell, C. Andersson, L. M. Kimerer, P. B. Cipolloni, K. H. Lim, S. D. Freedman, I. Bigio, B. P. Sachs, E. B. Hanlon, and L. T. Perelman, “Confocal light absorption and scattering spectroscopic microscopy monitors organelles in live cells with no exogenous labels,” PNAS, 104(44), 17255–17260 (2007).

19.   L. Qiu, V. Turzhitsky, R. Chuttani, D. Pleskow, J. D. Goldsmith, L. Guo, E. Vitkin, I. Itzkan, E. B. Hanlon, L. T. Perelman, “Spectral imaging with scattered light: from early cancer detection to cell biology,” IEEE J Sel Top Quantum Electron, 18(3), 1073-1083 (2012).19.