Rigorous Electromagnetic Model of Fourier Transform Infrared (FT-IR) Spectroscopic Imaging Applied to Automated Histology of Prostate Tissue Specimens
Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.
[1] D. Fernandez, R. Bhargava, S. Hewitt, and I. Levin, "Infrared spectroscopic
imaging for histopathologic recognition," Nature biotechnology,
vol. 23, no. 4, pp. 469-474, 2005.
[2] R. Bhargava, Analytical and bioanalytical chemistry, vol. 389, no. 4, pp.
1155-1169, 2007.
[3] C. Craver, "The coblentz society desk book of infrared spectra," DTIC
Document, Tech. Rep., 1977.
[4] E. Lewis, P. Treado, R. Reeder, G. Story, A. Dowrey, C. Marcott, and
I. Levin, "Fourier Transform Spectroscopic Imaging Using an Infrared
Focal-Plane Array Detector," Anal. Chem., vol. 67, no. 19, pp. 3377-
3381, 1995.
[5] M. Moharam and T. Gaylord, JOSA, vol. 71, no. 7, pp. 811-818, 1981.
[6] B. Davis, P. Carney, and R. Bhargava, "Theory of midinfrared absorption
microspectroscopy: I. homogeneous samples," Anal. Chem., vol. 82, no. 9,
pp. 3474-3486, 2010.
[7] ÔÇöÔÇö, "Theory of mid-infrared absorption microspectroscopy: Ii. heterogeneous
samples," Anal. Chem., vol. 82, no. 9, pp. 3487-3499, 2010.
[8] R. Reddy, B. Davis, P. Carney, and R. Bhargava, "Modeling fourier transform
infrared spectroscopic imaging of prostate and breast cancer tissue
specimens," IEEE International Symposium on Biomedical Imaging, pp.
738-741, 2011.
[9] B. Richards and E. Wolf, "Electromagnetic diffraction in optical systems.
II," Proceedings of the Royal Society of London. Series A, vol. 253, no.
1274, pp. 358-379, 1959.
[1] D. Fernandez, R. Bhargava, S. Hewitt, and I. Levin, "Infrared spectroscopic
imaging for histopathologic recognition," Nature biotechnology,
vol. 23, no. 4, pp. 469-474, 2005.
[2] R. Bhargava, Analytical and bioanalytical chemistry, vol. 389, no. 4, pp.
1155-1169, 2007.
[3] C. Craver, "The coblentz society desk book of infrared spectra," DTIC
Document, Tech. Rep., 1977.
[4] E. Lewis, P. Treado, R. Reeder, G. Story, A. Dowrey, C. Marcott, and
I. Levin, "Fourier Transform Spectroscopic Imaging Using an Infrared
Focal-Plane Array Detector," Anal. Chem., vol. 67, no. 19, pp. 3377-
3381, 1995.
[5] M. Moharam and T. Gaylord, JOSA, vol. 71, no. 7, pp. 811-818, 1981.
[6] B. Davis, P. Carney, and R. Bhargava, "Theory of midinfrared absorption
microspectroscopy: I. homogeneous samples," Anal. Chem., vol. 82, no. 9,
pp. 3474-3486, 2010.
[7] ÔÇöÔÇö, "Theory of mid-infrared absorption microspectroscopy: Ii. heterogeneous
samples," Anal. Chem., vol. 82, no. 9, pp. 3487-3499, 2010.
[8] R. Reddy, B. Davis, P. Carney, and R. Bhargava, "Modeling fourier transform
infrared spectroscopic imaging of prostate and breast cancer tissue
specimens," IEEE International Symposium on Biomedical Imaging, pp.
738-741, 2011.
[9] B. Richards and E. Wolf, "Electromagnetic diffraction in optical systems.
II," Proceedings of the Royal Society of London. Series A, vol. 253, no.
1274, pp. 358-379, 1959.
@article{"International Journal of Medical, Medicine and Health Sciences:49776", author = "Rohith K Reddy and David Mayerich and Michael Walsh and P Scott Carney and Rohit Bhargava", title = "Rigorous Electromagnetic Model of Fourier Transform Infrared (FT-IR) Spectroscopic Imaging Applied to Automated Histology of Prostate Tissue Specimens", abstract = "Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.", keywords = "Infrared, Spectroscopy, Imaging, Tissue classification", volume = "6", number = "3", pages = "39-5", }