Empirical study on the effects of acquisition parameters for FTIR hyperspectral imaging of brain tissue

• Published in: Analytical methods Vol. 12; no. 35; p. 4334
• Main Authors: Sacharz, J, Perez-Guaita, D, Kansiz, Mustafa, Nazeer, Shaiju S, Wesełucha-Birczyńska, A, Petratos, S, Wood, B R, Heraud, P
• Format: Journal Article
• Language: English
• Published: England 17-09-2020
• Subjects: Brain - diagnostic imaging; Cluster Analysis; Fourier Analysis; Humans; Hyperspectral Imaging; Spectroscopy, Fourier Transform Infrared
• ISSN: 1759-9679
• DOI: 10.1039/c9ay01200a

Fourier transform infrared (FTIR) spectroscopic imaging is a powerful technique for molecular imaging of pathologies associated with the nervous systems including multiple sclerosis research. However, there is no standard methodology or standardized protocol for FTIR imaging of tissue sections that maximize the ability to discriminate between the molecular, white and granular layers, which is essential in the investigation of the mechanism of demyelination process. Tissue sections are heterogeneous, complex and delicate, hence the parameters to generate high quality images in minimal time becomes essential in the modern clinical laboratory. This article presents an FTIR spectroscopic imaging study of post-mortem human brain tissue testing the effects of various measurement parameters and data analysis methods on image quality and acquisition time. Hyperspectral images acquired from the same region of a tissue using a range of the most common optical and collection parameters in different combinations were compared. These included magnification (4× and 15×), number of co-added scans (1, 4, 8, 16, 32, 64 and 128 scans) and spectral resolution (4, 8 and 16 cm-1). Images were compared in terms of acquisition time, signal-to-noise (S/N) ratio, and accuracy of the discrimination between three major tissue types in a section from the cerebellum (white matter, granular and molecular layers). In the latter case, unsupervised k-means cluster (KMC) analysis was employed to generate images from the hyperspectral images, which were compared to a reference image. The classification accuracy for tissue class discrimination was highest for the 4× magnifying objective, with 4 cm-1 spectral resolution and 128 co-added scans. The 15× magnifying objective gave the best accuracy for a spectral resolution of 4 cm-1 and 64 scans (96.3%), which was just above what was achieved using the 4× magnifying objective, with 4 cm-1 spectral resolution and 32 and 64 co-added scans (95.4 and 95.6%, respectively). These findings were correlated with a decrease in S/N ratio with increasing number of scans and was generally lower for the 15× objective. However, longer scan times were required using the 15× magnifying objective, which did not justify the very small improvement in the classification of tissue types.