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

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.

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

SUBMITTER: Madasamy A 

PROVIDER: S-EPMC9547608 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature