Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.

Journal: Journal of biomedical optics

Published Date: Oct 1, 2022

Abstract

SIGNIFICANCE: Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

Authors

Arumugaraj Madasamy

Indian Institute of Science, Department of Instrumentation and Applied Physics, Bengaluru, Karnataka, India, India.
Vipul Gujrati

Institute of Biological and Medical Imaging, Helmholtz Zentrum München (GmbH), Neuherberg, Germany, Germany.
Vasilis Ntziachristos

Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.
Jaya Prakash

Keywords

Deep Learning Image Processing, Computer-Assisted Phantoms, Imaging Photoacoustic Techniques Signal-To-Noise Ratio

External Resources

View on PubMed Access via DOI PubMed (36209354)

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.

Abstract

Authors

Keywords

External Resources

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