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.

Public Health Relevance

This project will result in methods for extraction of more information at higher quality and with estimated error bounds, making the technology more precise. The integrated hardware with software will enable real time corrections for instruments as well as extract parameters with the same accuracy for all samples, making information extracted invariant with operator and particular setup. This represents the next generation of instrumentation Cl that will he directly translatable to other users for wide dissemination

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM117594-02S1
Application #
9325172
Study Section
Special Emphasis Panel (ZGM1 (BM))
Program Officer
Hagan, Ann A
Project Start
2015-09-01
Project End
2019-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
2
Fiscal Year
2016
Total Cost
$35,750
Indirect Cost
Name
University of Texas Austin
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
170230239
City
Austin
State
TX
Country
United States
Zip Code
78712
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