Reconstructing FT-IR spectroscopic imaging data with a sparse prior

Authors:
Spencer P. Brady;Minh N. Do;Rohit Bhargava
Affiliations:
Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana Champaign;Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana Champaign;Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Fourier Transform Infrared (FT-IR) spectroscopic imaging is a potentially valuable tool for diagnosing breast and prostate cancer, but its clinical deployment is limited due to long data acquisition times and vast storage requirements. To counter this limitation, we develop a sparse representation for FT-IR absorbance spectra using a learned dictionary. This sparse representation is used as prior knowledge in regularizing the compressed sensing inverse problem. The data size and acquisition time are directly proportional to the length of the measured signal, namely the interferogram. Hence, we model our measurement process as interferogram truncation, which we implement by low pass filtering and downsampling in the spectral domain. With a downsample factor of four, our reconstruction is adequate for tissue classification and provides a Peak Signal-to-noise Ratio (PSNR) of 41.92 dB, while standard interpolation of the same low resolution measurements can only provide a PSNR of 36.93 dB.