Towards Translation of Discrete Frequency Infrared
Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy
Samples
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Abstract
Fourier transform infrared (FT-IR)
spectroscopic imaging has been
widely tested as a tool for stainless digital histology of biomedical
specimens, including for the identification of infiltration and fibrosis
in endomyocardial biopsy samples to assess transplant rejection. A
major barrier in clinical translation has been the slow speed of imaging.
To address this need, we tested and report here the viability of using
high speed discrete frequency infrared (DFIR) imaging to obtain stain-free
biochemical imaging in cardiovascular samples collected from patients.
Images obtained by this method were classified with high accuracy
by a Bayesian classification algorithm trained on FT-IR imaging data
as well as on DFIR data. A single spectral feature correlated with
instances of fibrosis, as identified by the pathologist, highlights
the advantage of the DFIR imaging approach for rapid detection. The
speed of digital pathologic recognition was at least 16 times faster
than the fastest FT-IR imaging instrument. These results indicate
that a fast, on-site identification of fibrosis using IR imaging has
potential for real time assistance during surgeries. Further, the
work describes development and applications of supervised classifiers
on DFIR imaging data, comparing classifiers developed on FT-IR and
DFIR imaging modalities and identifying specific spectral features
for accurate identification of fibrosis. This addresses a topic of
much debate on the use of training data and cross-modality validity
of IR measurements. Together, the work is a step toward addressing
a clinical diagnostic need at acquisition time scales that make IR
imaging technology practical for medical use