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PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics

Awasthi, Navchetan and Prabhakar, K Ram and Kalva, Sandeep Kumar and Pramanik, Manojit and Babu, R Venkatesh and Yalavarthy, Phaneendra K (2019) PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics. In: BIOMEDICAL OPTICS EXPRESS, 10 (5). pp. 2242-2258.

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Official URL: http://doi.org/10.1364/BOE.10.002227


The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data tbr training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental set-ups as well as imaging objects. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Item Type: Journal Article
Additional Information: Copyright of this article belongs to OPTICAL SOC AMER
Department/Centre: Division of Interdisciplinary Research > Computational and Data Sciences
Depositing User: LIS Interns
Date Deposited: 24 May 2019 11:04
Last Modified: 24 May 2019 11:04
URI: http://eprints.iisc.ac.in/id/eprint/62729

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