AI based image restoration algorithm for deep tissue imaging in photoacoustic system
Photoacoustic imaging is a new medical imaging technology that is safe and radiation-free, however, there is still a need for further improvement in spatial resolution and imaging speed as photoacoustic imaging moves towards clinical application. When photoacoustic imaging systems use sparsely sampl...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/165581 |
| _version_ | 1811688231990722560 |
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| author | Sun, Xiaoshi |
| author2 | Zheng Yuanjin |
| author_facet | Zheng Yuanjin Sun, Xiaoshi |
| author_sort | Sun, Xiaoshi |
| collection | NTU |
| description | Photoacoustic imaging is a new medical imaging technology that is safe and radiation-free, however, there is still a need for further improvement in spatial resolution and imaging speed as photoacoustic imaging moves towards clinical application. When photoacoustic imaging systems use sparsely sampled data to reconstruct images, traditional photoacoustic image reconstruction algorithms can produce artifacts that affect the quality of the images. In this dissertation, deep learning techniques are applied to reconstruct fuzzy undersampled photoacoustic data. Using the convolutional neural network (CNN) architecture, U-net and Fully Dense U-net (FD U-net) were chosen to improve the quality of photoacoustic images. The experimental results show that both networks are capable of performing the reconstruction task and can effectively handle blurred undersampled photoacoustic microscopy images. The results produced by the two approaches are also analyzed and compared in terms of reconstructed image quality. The FD U-net, which is an improvement on the U-net, has better performance in terms of reconstruction details. |
| first_indexed | 2024-10-01T05:28:56Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/165581 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:28:56Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1655812023-07-04T16:15:01Z AI based image restoration algorithm for deep tissue imaging in photoacoustic system Sun, Xiaoshi Zheng Yuanjin School of Electrical and Electronic Engineering YJZHENG@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Photoacoustic imaging is a new medical imaging technology that is safe and radiation-free, however, there is still a need for further improvement in spatial resolution and imaging speed as photoacoustic imaging moves towards clinical application. When photoacoustic imaging systems use sparsely sampled data to reconstruct images, traditional photoacoustic image reconstruction algorithms can produce artifacts that affect the quality of the images. In this dissertation, deep learning techniques are applied to reconstruct fuzzy undersampled photoacoustic data. Using the convolutional neural network (CNN) architecture, U-net and Fully Dense U-net (FD U-net) were chosen to improve the quality of photoacoustic images. The experimental results show that both networks are capable of performing the reconstruction task and can effectively handle blurred undersampled photoacoustic microscopy images. The results produced by the two approaches are also analyzed and compared in terms of reconstructed image quality. The FD U-net, which is an improvement on the U-net, has better performance in terms of reconstruction details. Master of Science (Signal Processing) 2023-04-03T02:29:00Z 2023-04-03T02:29:00Z 2023 Thesis-Master by Coursework Sun, X. (2023). AI based image restoration algorithm for deep tissue imaging in photoacoustic system. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165581 https://hdl.handle.net/10356/165581 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Sun, Xiaoshi AI based image restoration algorithm for deep tissue imaging in photoacoustic system |
| title | AI based image restoration algorithm for deep tissue imaging in photoacoustic system |
| title_full | AI based image restoration algorithm for deep tissue imaging in photoacoustic system |
| title_fullStr | AI based image restoration algorithm for deep tissue imaging in photoacoustic system |
| title_full_unstemmed | AI based image restoration algorithm for deep tissue imaging in photoacoustic system |
| title_short | AI based image restoration algorithm for deep tissue imaging in photoacoustic system |
| title_sort | ai based image restoration algorithm for deep tissue imaging in photoacoustic system |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/165581 |
| work_keys_str_mv | AT sunxiaoshi aibasedimagerestorationalgorithmfordeeptissueimaginginphotoacousticsystem |