Xiaohan Chang, Lingbo Cai, Jianlei Wang, Hongyang Dong, Jing Han, Chun Wang
{"title":"使用特征融合网络的糖尿病视网膜病变稀疏视图光声重建方法","authors":"Xiaohan Chang, Lingbo Cai, Jianlei Wang, Hongyang Dong, Jing Han, Chun Wang","doi":"10.1002/jbio.202400287","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.</p>\n </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"17 11","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sparse-View Photoacoustic Reconstruction Method for Diabetic Retinopathy Using Feature Fusion Network\",\"authors\":\"Xiaohan Chang, Lingbo Cai, Jianlei Wang, Hongyang Dong, Jing Han, Chun Wang\",\"doi\":\"10.1002/jbio.202400287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.</p>\\n </div>\",\"PeriodicalId\":184,\"journal\":{\"name\":\"Journal of Biophotonics\",\"volume\":\"17 11\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biophotonics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jbio.202400287\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biophotonics","FirstCategoryId":"101","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jbio.202400287","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Sparse-View Photoacoustic Reconstruction Method for Diabetic Retinopathy Using Feature Fusion Network
Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.
期刊介绍:
The first international journal dedicated to publishing reviews and original articles from this exciting field, the Journal of Biophotonics covers the broad range of research on interactions between light and biological material. The journal offers a platform where the physicist communicates with the biologist and where the clinical practitioner learns about the latest tools for the diagnosis of diseases. As such, the journal is highly interdisciplinary, publishing cutting edge research in the fields of life sciences, medicine, physics, chemistry, and engineering. The coverage extends from fundamental research to specific developments, while also including the latest applications.