Photoacoustic Imaging with a Finite-Size Circular Integrating Detector
Photoacoustic imaging (PAI) has rapidly developed in biomedical imaging. The point spread function (PSF) is critical for addressing image blurring in PAI. However, in circular integrating detection systems, the PSF exhibits spatial variations. This makes PSF extraction challenging. The existing stud...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4922 |
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| author | Shan Gao Xili Jing Mengyu Fang Jingru Zhao Tianrun Zhang |
| author_facet | Shan Gao Xili Jing Mengyu Fang Jingru Zhao Tianrun Zhang |
| author_sort | Shan Gao |
| collection | DOAJ |
| description | Photoacoustic imaging (PAI) has rapidly developed in biomedical imaging. The point spread function (PSF) is critical for addressing image blurring in PAI. However, in circular integrating detection systems, the PSF exhibits spatial variations. This makes PSF extraction challenging. The existing studies typically assume that the PSF is known or obtained through experiments. This study proposes a method for extracting the PSF based on the polar coordinate system. By transforming the image from the Cartesian coordinate system to the polar coordinate system, the “spin blur” problem is decomposed into multiple independent subproblems. With the separation of the radial and angular directions, the blurring kernel remains invariant at each radius, thereby simplifying the estimation of the PSF. To estimate the blurring kernel, we use polynomial algebraic common factor extraction techniques. The numerical simulation results validate the effectiveness of the method, and the impact of sample size on computational efficiency and accuracy is discussed. |
| format | Article |
| id | doaj-art-dd2342385bab4ffca694be3080984f64 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-dd2342385bab4ffca694be3080984f642025-08-20T02:58:43ZengMDPI AGApplied Sciences2076-34172025-04-01159492210.3390/app15094922Photoacoustic Imaging with a Finite-Size Circular Integrating DetectorShan Gao0Xili Jing1Mengyu Fang2Jingru Zhao3Tianrun Zhang4State Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, ChinaState Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, ChinaState Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, ChinaState Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, ChinaState Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, ChinaPhotoacoustic imaging (PAI) has rapidly developed in biomedical imaging. The point spread function (PSF) is critical for addressing image blurring in PAI. However, in circular integrating detection systems, the PSF exhibits spatial variations. This makes PSF extraction challenging. The existing studies typically assume that the PSF is known or obtained through experiments. This study proposes a method for extracting the PSF based on the polar coordinate system. By transforming the image from the Cartesian coordinate system to the polar coordinate system, the “spin blur” problem is decomposed into multiple independent subproblems. With the separation of the radial and angular directions, the blurring kernel remains invariant at each radius, thereby simplifying the estimation of the PSF. To estimate the blurring kernel, we use polynomial algebraic common factor extraction techniques. The numerical simulation results validate the effectiveness of the method, and the impact of sample size on computational efficiency and accuracy is discussed.https://www.mdpi.com/2076-3417/15/9/4922photoacoustic imagingcoordinate transformationdeconvolutionblurring kernel |
| spellingShingle | Shan Gao Xili Jing Mengyu Fang Jingru Zhao Tianrun Zhang Photoacoustic Imaging with a Finite-Size Circular Integrating Detector Applied Sciences photoacoustic imaging coordinate transformation deconvolution blurring kernel |
| title | Photoacoustic Imaging with a Finite-Size Circular Integrating Detector |
| title_full | Photoacoustic Imaging with a Finite-Size Circular Integrating Detector |
| title_fullStr | Photoacoustic Imaging with a Finite-Size Circular Integrating Detector |
| title_full_unstemmed | Photoacoustic Imaging with a Finite-Size Circular Integrating Detector |
| title_short | Photoacoustic Imaging with a Finite-Size Circular Integrating Detector |
| title_sort | photoacoustic imaging with a finite size circular integrating detector |
| topic | photoacoustic imaging coordinate transformation deconvolution blurring kernel |
| url | https://www.mdpi.com/2076-3417/15/9/4922 |
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