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Guolan Lu
PhD Proposal Presentation
Date: Wednesday January 21st, 2015
Time: 5:30 pm
Location: E160, Health Sciences Research Building (HSRB), Emory
Thesis Committee members:
Advisor: Baowei Fei, Ph.D. (BME/Radiology)
May Dongmei Wang, Ph.D. (BME)
John Oshinski, Ph.D. (BME/Radiology)
Georgia Zhuo Chen, Ph.D. (Emory)
Brian W. Pogue, Ph.D. (Dartmouth College)
Title:
Hyperspectral Imaging and Quantification for Cancer Detection and Image-Guided Surgery
Abstract:
Cancer is a leading cause of death in economically developed countries. Survival and quality of life of cancer patients are directly related to the stage at diagnosis. Early detection and subsequent surgical resection of tumor represents one of the most promising approaches to reducing the growing cancer burden. Advances in cancer treatment and improvements in cancer outcomes over the past few decades have been modest, despite significant investment in cancer research. Hyperspectral imaging (HSI) has emerged as promising modality for cancer detection and surgical-guidance. Although hyperspectral imaging has been extensively explored for earth surface observation by NASA, it has only recently been transferred for cancer imaging. The basic principle of HSI is to acquire a stack of two-dimensional (2D) images over continuous spectral bands across a wide range of electromagnetic spectra, e.g., from the ultraviolet (UV) to near-infrared (NIR) regions. HSI is a label-free, noninvasive, and nonionizing imaging technology that expands human vision from visible to the UV and NIR regions of light. The rationale for cancer detection with HSI lies on the fact that diffusely reflected light from tissue is influenced by the biochemical and morphological changes that occur as disease changes tissue pathology, hence spectral fingerprint captured by HSI carries diagnostic information which can be used to differentiate cancer from healthy tissue. Hyperspectral images, which contain spectral information at each image pixel, can be analyzed for the visualization, characterization and quantification of biological processes at the cellular, molecular, tissue and organ levels. Combined with quantitative data analysis methods, HSI is destined to play an increasingly important role in cancer management, from initial detection through treatment and follow-up. Therefore, this study proposes to investigate the utility of HSI in combination with quantitative image analysis methods for early cancer detection and image-guided surgery.
