Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples

Saumya Tiwari, Jai Raman, Vijaya Reddy, Andrew Ghetler, Richard P. Tella, Yang Han, Christopher R. Moon, Charles D. Hoke, Rohit Bhargava

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)10183-10190
Number of pages8
JournalAnalytical chemistry
Volume88
Issue number20
DOIs
StatePublished - Oct 18 2016

Fingerprint

Biopsy
Infrared imaging
Infrared radiation
Imaging techniques
Fourier transforms
Classifiers
Transplants
Histology
Infiltration
Discrete Fourier transforms
Fast Fourier transforms
Surgery
Coloring Agents

ASJC Scopus subject areas

  • Analytical Chemistry

Cite this

Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples. / Tiwari, Saumya; Raman, Jai; Reddy, Vijaya; Ghetler, Andrew; Tella, Richard P.; Han, Yang; Moon, Christopher R.; Hoke, Charles D.; Bhargava, Rohit.

In: Analytical chemistry, Vol. 88, No. 20, 18.10.2016, p. 10183-10190.

Research output: Contribution to journalArticle

Tiwari, Saumya ; Raman, Jai ; Reddy, Vijaya ; Ghetler, Andrew ; Tella, Richard P. ; Han, Yang ; Moon, Christopher R. ; Hoke, Charles D. ; Bhargava, Rohit. / Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples. In: Analytical chemistry. 2016 ; Vol. 88, No. 20. pp. 10183-10190.
@article{ce083f69c9c743d5818e2946c5dd2b2e,
title = "Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples",
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.",
author = "Saumya Tiwari and Jai Raman and Vijaya Reddy and Andrew Ghetler and Tella, {Richard P.} and Yang Han and Moon, {Christopher R.} and Hoke, {Charles D.} and Rohit Bhargava",
year = "2016",
month = "10",
day = "18",
doi = "10.1021/acs.analchem.6b02754",
language = "English (US)",
volume = "88",
pages = "10183--10190",
journal = "Analytical Chemistry",
issn = "0003-2700",
publisher = "American Chemical Society",
number = "20",

}

TY - JOUR

T1 - Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples

AU - Tiwari, Saumya

AU - Raman, Jai

AU - Reddy, Vijaya

AU - Ghetler, Andrew

AU - Tella, Richard P.

AU - Han, Yang

AU - Moon, Christopher R.

AU - Hoke, Charles D.

AU - Bhargava, Rohit

PY - 2016/10/18

Y1 - 2016/10/18

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84991756936&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84991756936&partnerID=8YFLogxK

U2 - 10.1021/acs.analchem.6b02754

DO - 10.1021/acs.analchem.6b02754

M3 - Article

C2 - 27626947

AN - SCOPUS:84991756936

VL - 88

SP - 10183

EP - 10190

JO - Analytical Chemistry

JF - Analytical Chemistry

SN - 0003-2700

IS - 20

ER -