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.
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
| Cao, Juan; Xiao, Yanyang; Chen, Zhonggui et al. (2018) Functional Data Approximation on Bounded Domains using Polygonal Finite Elements. Comput Aided Geom Des 63:149-163 |
| Gupta, Soumyajit; Bajaj, Chandrajit (2018) Efficient Clustering-based Noise Covariance Estimation for Maximum Noise Fraction. Natl Conf Comput Vis Pattern Recognit Image Process Graph 841:232-244 |
| Huang, Xiangru; Bajaj, Chandrajit; Liang, Zhenxiao et al. (2017) Translation Synchronization via Truncated Least Squares. Adv Neural Inf Process Syst 30:1459-1468 |
| Simões, Tiago; Lopes, Daniel; Dias, Sérgio et al. (2017) Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. Comput Graph Forum 36:643-683 |
| Inouye, David; Yang, Eunho; Allen, Genevera et al. (2017) A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution. Wiley Interdiscip Rev Comput Stat 9: |
| Zhang, Jiong; Yen, Ian E H; Ravikumar, Pradeep et al. (2017) Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. JMLR Workshop Conf Proc 54:1514-1522 |
| Öktem, Ozan; Chen, Chong; Domaniç, Nevzat Onur et al. (2017) SHAPE BASED IMAGE RECONSTRUCTION USING LINEARIZED DEFORMATIONS. Inverse Probl 33: |
| Huang, Xiangru; Huang, Qixing; Yen, Ian E H et al. (2017) Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. JMLR Workshop Conf Proc 54:1550-1559 |
| Inouye, David I; Ravikumar, Pradeep; Dhillon, Inderjit S (2016) Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. JMLR Workshop Conf Proc 48:2445-2453 |
| Bucero, Marta Abril; Bajaj, Chandrajit; Mourrain, Bernard (2016) On the construction of general cubature formula by flat extensions. Linear Algebra Appl 502:104-125 |
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