Madasamy, A and Gujrati, V and Ntziachristos, V and Prakash, J (2022) Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging. In: Journal of biomedical optics, 27 (10).
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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 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.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of biomedical optics |
| Publisher: | NLM (Medline) |
| Additional Information: | The copyright for this article belongs to the Author(S). |
| Keywords: | image processing; imaging phantom; photoacoustics; procedures; signal noise ratio, Deep Learning; Image Processing, Computer-Assisted; Phantoms, Imaging; Photoacoustic Techniques; Signal-To-Noise Ratio |
| Department/Centre: | Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics |
| Date Deposited: | 27 Oct 2022 09:23 |
| Last Modified: | 27 Oct 2022 09:23 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77633 |
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