Unsupervised deep learning approach for photoacoustic spectral unmixing

Deepit Abhishek Durairaj, Sumit Agrawal, Kerrick Johnstonbaugh, Haoyang Chen, Sri Phani Krishna Karri, Sri Rajasekhar Kothapalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In photoacoustic imaging, accurate spectral unmixing is required for revealing functional and molecular information of the tissue using multispectral photoacoustic imaging data. A significant challenge in deep-tissue photoacoustic imaging is the nonlinear dependence of the received photoacoustic signals on the local optical fluence and molecular distribution. To overcome this, we have deployed an end-to-end unsupervised neural network based on autoencoders. The proposed method employs the physical properties as the constraints to the neural network which effectively performs the unmixing and outputs the individual molecular concentration maps without a-priori knowledge of their absorption spectra. The algorithm is tested on a set of simulated multispectral photoacoustic images comprising of oxyhemoglobin, deoxy-hemoglobin and indocyanine green targets embedded inside a tissue mimicking medium. These in silico experiments demonstrated promising photoacoustic spectral unmixing results using a completely unsupervised deep learning approach.

Original languageEnglish (US)
Title of host publicationPhotons Plus Ultrasound
Subtitle of host publicationImaging and Sensing 2020
EditorsAlexander A. Oraevsky, Lihong V. Wang
PublisherSPIE
ISBN (Electronic)9781510632431
DOIs
StatePublished - 2020
EventPhotons Plus Ultrasound: Imaging and Sensing 2020 - San Francisco, United States
Duration: Feb 2 2020Feb 5 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11240
ISSN (Print)1605-7422

Conference

ConferencePhotons Plus Ultrasound: Imaging and Sensing 2020
CountryUnited States
CitySan Francisco
Period2/2/202/5/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Durairaj, D. A., Agrawal, S., Johnstonbaugh, K., Chen, H., Karri, S. P. K., & Kothapalli, S. R. (2020). Unsupervised deep learning approach for photoacoustic spectral unmixing. In A. A. Oraevsky, & L. V. Wang (Eds.), Photons Plus Ultrasound: Imaging and Sensing 2020 [112403H] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11240). SPIE. https://doi.org/10.1117/12.2546964