Photons Plus Ultrasound: Imaging and Sensing 2018 2018
DOI: 10.1117/12.2288353
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Reconstruction of initial pressure from limited view photoacoustic images using deep learning

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Cited by 34 publications
(17 citation statements)
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“…We chose a constant reduced scattering coefficient of 15 cm −1 in both background and vessel structures. Additional details on the simulation parameters can also be found in our previous work [53]. The raw time-series data was noised after k-space simulation with an additive Gaussian noise model of recorded noise of our system [54].…”
mentioning
confidence: 99%
“…We chose a constant reduced scattering coefficient of 15 cm −1 in both background and vessel structures. Additional details on the simulation parameters can also be found in our previous work [53]. The raw time-series data was noised after k-space simulation with an additive Gaussian noise model of recorded noise of our system [54].…”
mentioning
confidence: 99%
“…In comparison to other approaches, U-Net-based networks generally performed better than other architectures, e.g., compared to a simple three-layer CNN, 98 VGG, 101 and compared to applying U-Net directly to the measurement data g, 104 especially with respect to robustness. It is interesting that Antholzer et al 99 compare their results to a classic l 1 -regularization approach for compressed sensing and report that when the system matrix is randomly sampled, and hence undersampling artifacts change as well, the classical variational approach clearly outperforms the network-based postprocessing approach.…”
Section: Postprocessingmentioning
confidence: 94%
“…This implies that large multiscale networks are needed to transform the signal into the sought-after PAT image effectively. In early studies by Waibel et al 104 and Gröhl et al, 126 it was shown that using an asymmetric U-Net to reconstruct the PA image directly from raw sensor data is feasible in a limitedview setting. In comparison to a postprocessing approach using a U-Net, it was competitive in terms of mean reconstruction error, but exhibited a higher variance in reconstruction error.…”
Section: Convolutional Approachesmentioning
confidence: 99%
“…A total of 15580 sets of data are obtained, each of which contains a pair of low-quality images and ground truth, of which 80% are used for training, 10% for verification and the rest for testing. The conventional FBP [7] and deep learning-based (U-Net [21] ) methods are used as comparison methods.…”
Section: Experimental Configurementioning
confidence: 99%