Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review
Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8068 |
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| author | Thanh Dat Le Nchumpeni Chonpemo Shitiri Sung-Hoon Jung Seong-Young Kwon Changho Lee |
| author_facet | Thanh Dat Le Nchumpeni Chonpemo Shitiri Sung-Hoon Jung Seong-Young Kwon Changho Lee |
| author_sort | Thanh Dat Le |
| collection | DOAJ |
| description | Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes. By analyzing 30 of the newest publications in this field, we explain how deep learning models produce synthetic nuclear medicine images that closely resemble their real counterparts, significantly enhancing diagnostic accuracy when images are acquired at lower doses than the clinical policies’ standard. The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. In summary, our review underscores the significant potential of deep learning in NMI, indicating that synthetic image generation may be essential for addressing the existing limitations of NMI and improving patient outcomes. |
| format | Article |
| id | doaj-art-d9b3564e247b4fd18e3aab3f9a2c6f54 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d9b3564e247b4fd18e3aab3f9a2c6f542025-08-20T02:56:58ZengMDPI AGSensors1424-82202024-12-012424806810.3390/s24248068Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A ReviewThanh Dat Le0Nchumpeni Chonpemo Shitiri1Sung-Hoon Jung2Seong-Young Kwon3Changho Lee4Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Jeollanam-do, Republic of KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Jeollanam-do, Republic of KoreaDepartment of Hematology-Oncology, Chonnam National University Medical School, Chonnam National University Hwasun Hospital, Hwasun 58128, Jeollanam-do, Republic of KoreaDepartment of Nuclear Medicine, Chonnam National University Medical School, Chonnam National University Hwasun Hospital, Hwasun 58128, Jeollanam-do, Republic of KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Jeollanam-do, Republic of KoreaNuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes. By analyzing 30 of the newest publications in this field, we explain how deep learning models produce synthetic nuclear medicine images that closely resemble their real counterparts, significantly enhancing diagnostic accuracy when images are acquired at lower doses than the clinical policies’ standard. The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. In summary, our review underscores the significant potential of deep learning in NMI, indicating that synthetic image generation may be essential for addressing the existing limitations of NMI and improving patient outcomes.https://www.mdpi.com/1424-8220/24/24/8068nuclear medicine imagingsynthesizingtransforming |
| spellingShingle | Thanh Dat Le Nchumpeni Chonpemo Shitiri Sung-Hoon Jung Seong-Young Kwon Changho Lee Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review Sensors nuclear medicine imaging synthesizing transforming |
| title | Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review |
| title_full | Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review |
| title_fullStr | Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review |
| title_full_unstemmed | Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review |
| title_short | Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review |
| title_sort | image synthesis in nuclear medicine imaging with deep learning a review |
| topic | nuclear medicine imaging synthesizing transforming |
| url | https://www.mdpi.com/1424-8220/24/24/8068 |
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