Mr. Arumugaraj M. Profile

Arumugaraj M.

at Indian Institute of Science Bengaluru

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SPIE Journal Paper | 8 October 2022 Open Access

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

Arumugaraj Madasamy, Vipul Gujrati, Vasilis Ntziachristos, Jaya Prakash

JBO, Vol. 27, Issue 10, 106004, (October 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.10.106004

KEYWORDS: 3D modeling, Data modeling, Tissue optics, Absorption, Signal to noise ratio, Inverse optics, Image quality, Gallium nitride, Breast, Acoustics

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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.

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