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High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

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Published on: January 21, 2015

Model-based correction algorithm for Fourier Transform infrared microscopy measurements of complex tissue-substrate

Artur Dawid Surowka¹, Giovanni Birarda², Magdalena Szczerbowska-Boruchowska³

¹Elettra-Sincrotrone Trieste, Strada Statale 14 - km 163.5, 34149, Basovizza, Trieste, Italy; AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, al. Mickiewicza 30, 30-059, Kraków, Poland.

Analytica Chimica Acta

|February 22, 2020

Summary

A new automated algorithm, Multiple Linear Regression Multi-Reference (MLR-MR), enhances Fourier transform infrared (FTIR) imaging of complex tissues. This method improves spectral quality and histological accuracy for better biological sample analysis.

Keywords:

Data analysis Data pre-processing Fourier transform infrared micro-spectroscopy Micro-imaging Scattering correction

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Area of Science:

Biomedical Imaging
Spectroscopy
Computational Biology

Background:

Model-based algorithms improve Fourier transform infrared (FTIR) spectroscopy data pre-processing.
Existing methods struggle with large-area FTIR imaging of complex tissues.
Scattering and fringing effects reduce spectral quality in biological samples.

Purpose of the Study:

To develop a fully-automated algorithm for FTIR imaging of complex biological tissues.
To correct linear baseline effects, substrate inhomogeneity, and chemical heterogeneity.
To enhance the histological fidelity of FTIR imaging data.

Main Methods:

Development of an integrated Multiple Linear Regression Multi-Reference (MLR-MR) method.
Utilizing multiple-reference spectra for histologically heterogeneous samples.
Application to FTIR imaging of rat brain frontal cortex tissue.

Main Results:

The MLR-MR algorithm effectively corrects baseline effects and compensates for sample heterogeneity.
Achieved "pure" absorbance spectra and significantly improved histological fidelity compared to single-reference methods.
Demonstrated potential for extracting substrate thickness information and evaluating topography.

Conclusions:

The MLR-MR method provides a robust solution for analyzing complex biological tissues using FTIR imaging.
This approach enhances spectral quality and histological accuracy, enabling more reliable data interpretation.
The algorithm is adaptable for complex systems requiring the elimination of Mie scattering effects.