The Optics and Medicine Laboratory would like to welcome its newest graduate student, Yan Zhao, to Hanover. Originally from Xuchang, China, Yan received his bachelors degree in engineering from Xian Jiaotong University. While at Jiaotong, Yan published a paper titled “Polarization dressings of four-wave mixing process in a V-type three-level atomic system” in Optics Communications. Published with a number of other students, this paper examines the interplay of four coexisting four-wave mixing (FWM) signals and focuses on how these waves act in atomic systems.
This is Yan’s first time coming to the United States. After landing in New York, Yan traveled to Hanover via coach, and started moving into his new office in Thayer. So far, he finds New Hampshire “amazing and interesting” and looks forward to meeting other incoming graduate students.
Outside the lab Yan plays tennis and badminton, and enjoys reading science fiction novels.
The research fellowship of $100,000, which is distributed over a two year period, has allowed Ken to develop a non-invasive method for measuring the distribution of human epidermal growth factor receptor 2 (HER2)—an important molecular target in breast cancer therapy—across the tissue of a tumor. Prior research has connected both the aggressiveness of a breast cancer tumor and its resistance to chemotherapy to the concentration of HER2 in vivo, but the biopsies used to measure the concentration of HER2 only sampled a small section of these tumors. The CIHR Postdoctoral fellowship is now allowing Ken to map the distribution of HER2 across breast cancer tumors using fluorescence molecular imaging.
Currently, Ken is expanding the methodology that he developed in his examination of HER2 in breast cancer to develop a method to non-invasively determine metastatic tumor burden in sentinel lymph nodes. Ken hopes that his application of this methodology will one day yield a non-invasive test using fluorescent imaging to optically examine a lymph node and determine whether it contains cancerous cells before removing it surgically.
Scattered light measured from tissue can be uniquely correlated to tissue substructure, function and progression of disease. The ultrastructural information provided by scatter may render optical techniques valuable to diagnosis.
Many recent studies have demonstrated that scattered light measured from tissue can be uniquely correlated to tissue substructure, function and progression of disease, if the wavelength dependence of the light is obtained at each pixel. This is because the morphologic changes associated with cancer progression cause organelle and structural matrix alteration, which can be observed macroscopically as local fluctuations in the optical refractive index (RI). These changes include hyper-proliferation of epithelium, nuclear crowding and enlargement, as well as intracellular organelle changes and sub-cellular stromal matrix alteration. Therefore, it is quite reasonable to assume that the ultrastructural information provided by scatter may render optical techniques valuable to diagnosis; the limiting factor being our lack of knowledge about how light scatters through heterogeneous tissues.
Extracting information from scatter spectra requires an ability to reasonably model the behavior of light as it passes through a tissue. This is a rather convoluted problem because it is difficult to separate light that has weakly scattered from that which has multiply scattered, in addition the effects of absorption and scatter are intermingled. To circumvent this complication, optical constraints are applied to limit detected photons to those primarily experiencing a single elastic collision. A raster scanning reflectance spectroscopy imaging system is used to characterize fresh, excised tumors and normal specimens with 100 micron spatial resolution (approximately one mean free scattering path length in tissue). This system was designed to sample the scatter directly, allowing empirical separation of the absorption and scattering effects. Scatter measures are then elucidated with pathology so that diagnostic categories of breast tissue may be optically characterized for a classification algorithm.
To enhance the diagnostic utility of our system, we are also using electron microscopy to visualize individual, sub-wavelength scatterers to better understand how the distribution of small scatterers in the extra-cellular matrix influences optical signals. The focus of this analysis is on collagen fibers because scattering from epithelial cells is well approximated by Mie theory and little is known about collagen, a dominant, non-spherical scatterer. Understanding light-tissue interactions at the microscopic level will improve models of light propagation through breast tissue and consequently data parameterization.
IG-NIRS provides deep tissue functional characterization at high resolution. This approach combines conventional imaging techniques such as MRI and CT with optical NIR technologies, giving information directly relating to the vascular and metabolic status of tissue in-vivo.
Image-guided near infrared spectroscopy (IG-NIRS) provides deep tissue functional characterization at high resolution. This approach combines conventional imaging techniques such as MRI and CT with optical near infrared technologies, giving information directly relating to the vascular and metabolic status of tissue in-vivo. The resultant estimates of total hemoglobin, oxygen saturation, water, lipids and scatter provide a window towards understanding the mechanisms of cancer in terms of angiogenesis, hypoxia, changes in the interstitium and cell organelle structural changes. This type of spectroscopy has been applied for breast cancer diagnosis and treatment monitoring, as well as image-guided fluorescence in small-animals.
Optimization of these systems is essential to provide quantitative and accurate spectroscopy. This optimization encompasses system design for simultaneous multi-modality image acquisition, methods for intelligently combining spatial anatomical structure from MRI/CT into optical recovery, image segmentation, visualization and interpretation of novel combined optical and MRI/CT parameters.
The focus of this project is optimization to go from image segmentation of MRI volume to recovery of spectroscopic parameters in 3-D in a seamless automated manner, in 3-D. Funded by NIHNIBIB, computationally efficient models have been developed using finite element (FE) and boundary element methods (BEM). BEM is specific to IG-NIRS using only surface discretization whereas FEM allows for NIR tomography using volume discretization. Both models solve for light propagation in tissue using diffusion equation. Software packages have been developed using FEM (NIRFAST) and BEM (BEMFAST) to run in multi-processing cluster. Segmentation tools and visualization based on ITK and VTK have been developed specific to this multi-modality setting.
Our goals are to study breast tissue in-vivo and monitoring different tissue responses to neoadjuvant chemotherapy using this multi-modality approach. Cancer shows higher total hemoglobin compared to benign lesions. In addition, total hemoglobin levels appear to be sensitive to how the patient is responding to neoadjuvant chemotherapy. We are also studying image-guided fluorescence in small animals as a precursor to clinical work in fluorescence.