It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Acoustic-resolution photoacoustic microscopy (AR-PAM) enables visualization of biological tissues at depths of several millimeters with superior optical absorption contrast. However, the lateral resolution and sensitivity of AR-PAM are generally lower than those of optical-resolution PAM (OR-PAM) owing to the intrinsic physical acoustic focusing mechanism. Here, we demonstrate a computational strategy with two generative adversarial networks (GANs) to perform semi/unsupervised reconstruction with high resolution and sensitivity in AR-PAM by maintaining its imaging capability at enhanced depths. The b-scan PAM images were prepared as paired (for semi-supervised conditional GAN) and unpaired (for unsupervised CycleGAN) groups for label-free reconstructed AR-PAM b-scan image generation and training. The semi/unsupervised GANs successfully improved resolution and sensitivity in a phantom and in vivo mouse ear test with ground truth. We also confirmed that GANs could enhance resolution and sensitivity of deep tissues without the ground truth.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Chonnam National University, Department of Artificial Intelligence Convergence, Gwangju, Korea (GRID:grid.14005.30) (ISNI:0000 0001 0356 9399)
2 Chonnam National University Medical School and Hwasun Hospital, Department of Nuclear Medicine, Hwasun-gun, Korea (GRID:grid.14005.30) (ISNI:0000 0001 0356 9399)
3 Chonnam National University, Department of Artificial Intelligence Convergence, Gwangju, Korea (GRID:grid.14005.30) (ISNI:0000 0001 0356 9399); Chonnam National University Medical School and Hwasun Hospital, Department of Nuclear Medicine, Hwasun-gun, Korea (GRID:grid.14005.30) (ISNI:0000 0001 0356 9399)