PuSH - Publication Server of Helmholtz Zentrum München: Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

Madasamy, A.* ; Gujrati, V. ; Ntziachristos, V. ; Prakash, J.*

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

J. Biomed. Opt. 27:106004 (2022)

Postprint

Research data

DOI

PMC

	Open Access Green

Abstract
Metrics
Extra information

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. AIM: Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium. APPROACH: Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets. RESULTS: The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction. CONCLUSIONS: The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Deep Learning ; Fluence Correction ; Image Deconvolution ; Image Reconstruction ; Optoacoustic Imaging

ISSN (print) / ISBN 1083-3668

e-ISSN 1560-2281

Journal Journal of Biomedical Optics

Quellenangaben Volume: 27, Issue: 10, Article Number: 106004

Publisher SPIE

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Biological and Medical Imaging (IBMI)