Deep-learning-based electrical noise removal for localized spectral optoacoustic contrast in deep tissue

Dehner C, Olefir I, Chowdhury KB, Juestel D, Ntziachristos V (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Publisher: SPIE

Book Volume: 11923

Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Event location: Virtual, Online, DEU

ISBN: 9781510647121

DOI: 10.1117/12.2614318

Abstract

Image contrast in multispectral optoacoustic tomography can be reduced by electrical noise. We present a deep learning method to remove electrical noise from optoacoustic signals and thereby significantly enhance morphological and spectral contrast.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Dehner, C., Olefir, I., Chowdhury, K.B., Juestel, D., & Ntziachristos, V. (2021). Deep-learning-based electrical noise removal for localized spectral optoacoustic contrast in deep tissue. In Chulhong Kim, Jan Laufer, Roger J. Zemp (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Virtual, Online, DEU: SPIE.

MLA:

Dehner, Christoph, et al. "Deep-learning-based electrical noise removal for localized spectral optoacoustic contrast in deep tissue." Proceedings of the Opto-Acoustic Methods and Applications in Biophotonics V 2021, Virtual, Online, DEU Ed. Chulhong Kim, Jan Laufer, Roger J. Zemp, SPIE, 2021.

BibTeX: Download