Abstract
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 for 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.
Highlights
Photoacoustic (PA) imaging is a non-invasive imaging modality which renders optical resolution at ultrasonic depth inside the biological tissue [1,2,3,4]
The solution obtained via optimal choice of k and δ using error estimate method will be refereed as xLTO, with LTO referring to Lanczos Tikhonov solution obtained via Optimal choice of reconstruction parameters
Root Mean Square Error (RMSE) and Contrastto-Noise Ratio (CNR) were employed as the target initial pressure was known for these, while for experimental phantoms signal to noise ratio (SNR) was used as the evaluation criteria
Summary
Photoacoustic (PA) imaging is a non-invasive imaging modality which renders optical resolution at ultrasonic depth inside the biological tissue [1,2,3,4]. Photoacoustic tomography involves solving the initial problem by utilizing the data acquired at the boundary at time “t” to compute the initial pressure distribution at time “t” = 0 This initial value problem is solved using several reconstruction techniques. Introduced modified guided filtering [29] based fusion method in PA imaging was shown to improve the reconstructed images It uses a set of regularization parameters, which need to be selected carefully, to obtain the final image output. The proposed method (termed as PA-Fuse) was evaluated at different noise levels to show the robustness of the architecture It was assessed with inputs other than the network was trained for and shown that the reconstruction results obtained were conforming to broad utility. To show the universal utility of the proposed PA-Fuse method, reconstructed results from different experimental setups were utilized to improve the reconstruction results without retraining the network
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