Journal Article

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

Ntziachristos, Vasilis, Prakash, Jaya, Gujrati, Vipul, Madasamy, Arumugaraj

2022

Overview

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.

• Publisher: Society of Photo-Optical Instrumentation Engineers,SPIE,S P I E - International Society for
• Subject: Absorption / Absorptivity / Acoustics / Blood vessels / Compensation / Deep Learning / Diffusion rate / Fluence / Generative adversarial networks / Image Processing, Computer-Assisted - methods / Image quality / Image reconstruction / Imaging / Inverse problems / Light / Medical imaging equipment / Methods / Morphology / Nonlinear optics / Optical properties / Phantoms, Imaging / Photoacoustic Techniques - methods / Propagation / Recovery / Signal to noise ratio / Tissues / Tomography / Ultrasonic imaging