Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach
Photoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires regularization to obtain meaningful solutions....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-01-01
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| Series: | Molecular Imaging |
| Online Access: | http://dx.doi.org/10.1155/2022/7877049 |
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| _version_ | 1841561930760716288 |
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| author | Xueyan Liu Shuo Dai Mengyu Wang Yining Zhang |
| author_facet | Xueyan Liu Shuo Dai Mengyu Wang Yining Zhang |
| author_sort | Xueyan Liu |
| collection | DOAJ |
| description | Photoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires regularization to obtain meaningful solutions. The purpose of the work is to propose an elastic network (EN) model to improve the quality of reconstructed photoacoustic images. To evaluate the performance of the proposed method, a series of numerical simulations and tissue-mimicking phantom experiments are performed. The experiment results indicate that, compared with the L1-norm and L2-normbased regularization methods with different numerical phantoms, Gaussian noise of 10-50 dB, and different regularization parameters, the EN method with α=0.5 has better image quality, calculation speed, and antinoise ability. |
| format | Article |
| id | doaj-art-702a9f139a454d049e09b1e7d353d2db |
| institution | Kabale University |
| issn | 1536-0121 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Molecular Imaging |
| spelling | doaj-art-702a9f139a454d049e09b1e7d353d2db2025-01-03T01:19:29ZengSAGE PublishingMolecular Imaging1536-01212022-01-01202210.1155/2022/7877049Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net ApproachXueyan Liu0Shuo Dai1Mengyu Wang2Yining Zhang3School of Mathematical SciencesSchool of Mathematical SciencesSchool of Mathematical SciencesSchool of Mathematical SciencesPhotoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires regularization to obtain meaningful solutions. The purpose of the work is to propose an elastic network (EN) model to improve the quality of reconstructed photoacoustic images. To evaluate the performance of the proposed method, a series of numerical simulations and tissue-mimicking phantom experiments are performed. The experiment results indicate that, compared with the L1-norm and L2-normbased regularization methods with different numerical phantoms, Gaussian noise of 10-50 dB, and different regularization parameters, the EN method with α=0.5 has better image quality, calculation speed, and antinoise ability.http://dx.doi.org/10.1155/2022/7877049 |
| spellingShingle | Xueyan Liu Shuo Dai Mengyu Wang Yining Zhang Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach Molecular Imaging |
| title | Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach |
| title_full | Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach |
| title_fullStr | Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach |
| title_full_unstemmed | Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach |
| title_short | Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach |
| title_sort | compressed sensing photoacoustic imaging reconstruction using elastic net approach |
| url | http://dx.doi.org/10.1155/2022/7877049 |
| work_keys_str_mv | AT xueyanliu compressedsensingphotoacousticimagingreconstructionusingelasticnetapproach AT shuodai compressedsensingphotoacousticimagingreconstructionusingelasticnetapproach AT mengyuwang compressedsensingphotoacousticimagingreconstructionusingelasticnetapproach AT yiningzhang compressedsensingphotoacousticimagingreconstructionusingelasticnetapproach |