Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network
Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing meth...
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/22/8089 |
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| author | Panpan Chen Chengcheng Liu Ting Feng Yong Li Dean Ta |
| author_facet | Panpan Chen Chengcheng Liu Ting Feng Yong Li Dean Ta |
| author_sort | Panpan Chen |
| collection | DOAJ |
| description | Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection. |
| first_indexed | 2024-03-10T14:50:06Z |
| format | Article |
| id | doaj.art-bfde5739f8b3411fa0a6263f335c84d3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:50:06Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bfde5739f8b3411fa0a6263f335c84d32023-11-20T21:02:51ZengMDPI AGApplied Sciences2076-34172020-11-011022808910.3390/app10228089Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning NetworkPanpan Chen0Chengcheng Liu1Ting Feng2Yong Li3Dean Ta4Institute of Acoustics, School of Physical Science and Engineering, Tongji University, Shanghai 200092, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaDepartment of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaInstitute of Acoustics, School of Physical Science and Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Electronic Engineering, Fudan University, Shanghai 200433, ChinaPhotoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection.https://www.mdpi.com/2076-3417/10/22/8089photoacoustic imagingbone structureconvolution neural network (CNN)attention |
| spellingShingle | Panpan Chen Chengcheng Liu Ting Feng Yong Li Dean Ta Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network Applied Sciences photoacoustic imaging bone structure convolution neural network (CNN) attention |
| title | Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network |
| title_full | Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network |
| title_fullStr | Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network |
| title_full_unstemmed | Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network |
| title_short | Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network |
| title_sort | improved photoacoustic imaging of numerical bone model based on attention block u net deep learning network |
| topic | photoacoustic imaging bone structure convolution neural network (CNN) attention |
| url | https://www.mdpi.com/2076-3417/10/22/8089 |
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