1-page abstract for submitted to the URSI Commission K as part of the 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting.
Spectroscopic Terahertz (THz) imaging has demonstrated utility in several biomedical applications, including identification of tissue malignancies (M.-A. Brun, F. Formanek, A. Yasuda, M. Sekine, N. Ando, and Y. Eishii, “Terahertz imaging applied to cancer diagnosis,” Phys. Med. Biol., vol. 55, pp. 4615–4623, 2010), detection of axon demyelination in human brain due to Alzheimer’s (W.-G. Yeo, O. Gurel, N. Srinivasan, P.D. King, N.K. Nahar, S. Park, N.L. Lehman, and K. Sertel, “Terahertz Imaging and Electromagnetic Model of Axon Demyelination in Alzheimer's Disease”, IEEE Transactions on Terahertz Science and Technology, Volume: 7, Issue: 6, pp. 711 – 721, August 2017), and skin burn healing (M. H. Arbab, D. P.Winebrenner, T. C. Dickey, A. Chen, M. B. Klein, and P. D. Mourad, “Terahertz spectroscopy for the assessment of burn injuries in vivo,” J. Biomed. Opt., vol. 18, no. 7, 2013, No. 077044), to name a few. Although the state of the art THz instrumentation is effective in capturing the reflection and/or transmission spectral of biomedical samples, the assessment of these images in a diagnostic setting almost always requires concurrent microscopic assessment of sectioned and stained samples of the same tissue conducted by a trained pathologist.
In an effort to break out of this conundrum and demonstrate potential of THz imaging for diagnostic utility, here we propose machine learning as a tool to automatically classify biomedical images and determine the regions of interest. In particular, we present a vector-network-analyzer-based, fully-polarimetric THz imaging setup with 3.4× improved resolution, operating at 1THz to capture images of formalin-fixed, paraffin-embedded human brain tissue samples to identify regions most impacted by the demyelination of the axons. Due to the elongated morphology of the axon bundles, the 4-fold polarimetric reflection image data provides additional information to effectively classify such tissues.
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Three-step one-way model in terahertz biomedical detection
Abstract Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.
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- Award ID(s):
- 1916068
- NSF-PAR ID:
- 10333030
- Date Published:
- Journal Name:
- PhotoniX
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2662-1991
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s43074-021-00034-0
