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Cascade neural approximating for few-shot super-resolution photoacoustic angiography
- Source :
- Applied Physics Letters. 121:103701
- Publication Year :
- 2022
- Publisher :
- AIP Publishing, 2022.
-
Abstract
- High-resolution photoacoustic angiography images are reconstructed from undersampled images with the help of a super-resolution deep neural network, enhancing the ability of the photoacoustic angiography systems to image dynamic processes in living tissues. However, image degradations are difficult to estimate due to a lack of knowledge of the point spread function and noise sources, resulting in poor generalization capability of the trained super-resolution model. In this work, a high-order residual cascade neural network was developed to reconstruct high-resolution vascular images, which is a neural approximating approach used to remove image degradations of photoacoustic angiography. To handle overfitting in training super-resolution model with a limited dataset, we proposed a BicycleGAN based image synthesis method in data preparation, achieving a strong regularization by forging realistic photoacoustic vascular images that act to essentially increase the training dataset. The quantitative analysis of the reconstructed results shows that the high-order residual cascade neural network surpassed the other residual super-resolution neural networks. Most importantly, we demonstrated that the generalized model could be achieved despite the limited training dataset, promising to be a methodology for few-shot super-resolution photoacoustic angiography.
- Subjects :
- Physics and Astronomy (miscellaneous)
Subjects
Details
- ISSN :
- 10773118 and 00036951
- Volume :
- 121
- Database :
- OpenAIRE
- Journal :
- Applied Physics Letters
- Accession number :
- edsair.doi...........f7d7103bfcb554775fcaf27f761d006c
- Full Text :
- https://doi.org/10.1063/5.0100424