Mathematical Chemical Imaging with Uncertainty Quantification

University Of Texas At Austin, Austin TX

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Abstract

? DESCRIPTION (provided by applicant): A key theme in biomedical sciences today is the integration of spatial and molecular information to predict the behavior of complex systems. Chemical imaging (CI) is an exciting new paradigm that simultaneously records both spatial structure and molecular spectral information from a sample, promising to considerably extend current microscopy methods. Recent advances in mid-infrared (IR) Cl imaging are now allowing rapid recording of full image sets within minutes, enabling a wide variety of applications ranging from visualizing diffusion in skin, to biomaterial evaluation to cancer pathology. IR Cl data typically consist of 10 megapixels, with each pixel containing 2000 spectral frequencies and an absorbance value between 0 and 1 (with noise levels from 10-4 to 0.1, depending on experimental parameters) at each frequency. As opposed to molecular probes or dyes conventionally used in biomedical imaging, computational tools are the only route to extracting information from Cl data. The barriers limiting progress today are that recorded data are exceptionally large (100GB), absorbance at all wave numbers may not contain useful knowledge, some frequencies have redundant information and a relatively high signal to noise ratio (SNR) of 1000:1 is often required. The full potential of Cl cannot be realized and useful biomedical imaging is impossible until these challenges are met. The goal of this collaboration between a computational mathematics group and a spectroscopic imaging group is to do address extant Cl challenges in a novel manner. Our complementary expertise will develop fundamentally new methods for extracting knowledge and integrate them into instrumentation to transform the practice of IR Cl and make it confidently usable by the biomedical scientist.