MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data
Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detecti...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Photoacoustics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221359792400096X |
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| author | Liangjie Wang Yi-Chao Meng Yiming Qian |
| author_facet | Liangjie Wang Yi-Chao Meng Yiming Qian |
| author_sort | Liangjie Wang |
| collection | DOAJ |
| description | Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data. In this work, we propose an improved convolutional neural network (CNN) architecture, called multi-scale dense UNet (MSD-Net), to correct artifacts in 2D photoacoustic tomography (PAT). MSD-Net exploits the advantages of multi-scale information fusion and dense connections to improve the performance of CNN. Experimental validation with both simulated and in vivo datasets demonstrates that our method achieves better reconstructions with improved speed. |
| format | Article |
| id | doaj-art-e3c0000b8bfb49bbab3e39efc195e506 |
| institution | DOAJ |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-e3c0000b8bfb49bbab3e39efc195e5062025-08-20T02:46:40ZengElsevierPhotoacoustics2213-59792025-02-014110067910.1016/j.pacs.2024.100679MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse dataLiangjie Wang0Yi-Chao Meng1Yiming Qian2Institute of Fiber Optics, Shanghai University, Shanghai 201800, China; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, ChinaInstitute of Fiber Optics, Shanghai University, Shanghai 201800, China; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China; Correspondence to: Institute of Fiber Optics, School of Communication and Information Engineering, Shanghai University.Institute of Fiber Optics, Shanghai University, Shanghai 201800, China; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, ChinaPhotoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data. In this work, we propose an improved convolutional neural network (CNN) architecture, called multi-scale dense UNet (MSD-Net), to correct artifacts in 2D photoacoustic tomography (PAT). MSD-Net exploits the advantages of multi-scale information fusion and dense connections to improve the performance of CNN. Experimental validation with both simulated and in vivo datasets demonstrates that our method achieves better reconstructions with improved speed.http://www.sciencedirect.com/science/article/pii/S221359792400096XBiomedical imagingPhotoacoustic imagingDeep learningConvolutional networks |
| spellingShingle | Liangjie Wang Yi-Chao Meng Yiming Qian MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data Photoacoustics Biomedical imaging Photoacoustic imaging Deep learning Convolutional networks |
| title | MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data |
| title_full | MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data |
| title_fullStr | MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data |
| title_full_unstemmed | MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data |
| title_short | MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data |
| title_sort | msd net multi scale dense convolutional neural network for photoacoustic image reconstruction with sparse data |
| topic | Biomedical imaging Photoacoustic imaging Deep learning Convolutional networks |
| url | http://www.sciencedirect.com/science/article/pii/S221359792400096X |
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