Cascade neural approximating for few-shot super-resolution photoacoustic angiography

被引:5
|
作者
Ma, Yuanzheng [1 ,2 ]
Xiong, Kedi [1 ,2 ,3 ]
Hou, Xuefei [3 ]
Zhang, Wuyu [1 ,2 ]
Chen, Xin [1 ,2 ]
Li, Ling [3 ]
Yang, Sihua [1 ,2 ,3 ]
机构
[1] South China Normal Univ, MOE Key Lab Laser Life Sci, Coll Biophoton, Guangzhou 510631, Peoples R China
[2] South China Normal Univ, Inst Laser Life Sci, Coll Biophoton, Guangzhou 510631, Peoples R China
[3] South China Normal Univ, Coll Biophoton, Guangdong Prov Key Lab Laser Life Sci, Guangzhou 510631, Peoples R China
基金
中国国家自然科学基金;
关键词
NETWORKS;
D O I
10.1063/5.0100424
中图分类号
O59 [应用物理学];
学科分类号
摘要
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.
引用
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页数:7
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