SlingBAG: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging
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| Publicado en: | Nature Communications vol. 17, no. 1 (2026), p. 128-141 |
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| Otros Autores: | , , , , , , , |
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| 024 | 7 | |a 10.1038/s41467-025-66855-w |2 doi | |
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| 045 | 2 | |b d20260101 |b d20261231 | |
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| 100 | 1 | |a Li, Shuang |u Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China (ROR: https://ror.org/02v51f717) (GRID: grid.11135.37) (ISNI: 0000 0001 2256 9319) | |
| 245 | 1 | |a SlingBAG: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging | |
| 260 | |b Nature Publishing Group |c 2026 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Large-scale 3D photoacoustic imaging has become increasingly important for both clinical and pre-clinical applications. Limited by cost and system complexity, only systems with sparsely-distributed sensors can be widely implemented, which necessitates advanced reconstruction algorithms to reduce artifacts. However, the high computing memory and time consumption of traditional iterative reconstruction (IR) algorithms is practically unacceptable for large-scale 3D photoacoustic imaging. Here, we propose a point cloud-based IR algorithm that reduces memory consumption by several orders, wherein the 3D photoacoustic scene is modeled as a series of Gaussian-distributed spherical sources stored in form of point cloud. During the IR process, not only are properties of each Gaussian source, including its peak intensity (initial pressure value), standard deviation (size) and mean (position) continuously optimized, but also each Gaussian source itself adaptively undergoes destroying, splitting, and duplication along the gradient direction. This method, named SlingBAG, the sliding Gaussian ball adaptive growth algorithm, enables high-quality large-scale 3D photoacoustic reconstruction with fast iteration and extremely low memory usage. We validated the SlingBAG algorithm in both simulation study and in vivo animal experiments.Researchers present SlingBAG, an iterative reconstruction algorithm for large-scale 3D photoacoustic imaging. It uses an adaptive point cloud model to achieve high-quality imaging from sparse data, notably cutting cost in both memory and time. | |
| 653 | |a Sparsity | ||
| 653 | |a Iterative algorithms | ||
| 653 | |a 3-D graphics | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Algorithms | ||
| 653 | |a Cloud computing | ||
| 653 | |a Sensors | ||
| 653 | |a Three dimensional models | ||
| 653 | |a Initial pressure | ||
| 653 | |a Arrays | ||
| 653 | |a In vivo methods and tests | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wang, Yibing |u Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China (ROR: https://ror.org/02v51f717) (GRID: grid.11135.37) (ISNI: 0000 0001 2256 9319) | |
| 700 | 1 | |a Gao, Jian |u School of Intelligence Science and Technology, Nanjing University, Suzhou, China (ROR: https://ror.org/01rxvg760) (GRID: grid.41156.37) (ISNI: 0000 0001 2314 964X) | |
| 700 | 1 | |a Kim, Chulhong |u Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Department of Medical Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007) | |
| 700 | 1 | |a Choi, Seongwook |u Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Department of Medical Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007); Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, Republic of Korea (ROR: https://ror.org/04xysgw12) (GRID: grid.49100.3c) (ISNI: 0000 0001 0742 4007) | |
| 700 | 1 | |a Zhang, Yu |u Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China (ROR: https://ror.org/02v51f717) (GRID: grid.11135.37) (ISNI: 0000 0001 2256 9319) | |
| 700 | 1 | |a Chen, Qian |u Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China (ROR: https://ror.org/02v51f717) (GRID: grid.11135.37) (ISNI: 0000 0001 2256 9319) | |
| 700 | 1 | |a Yao, Yao |u School of Intelligence Science and Technology, Nanjing University, Suzhou, China (ROR: https://ror.org/01rxvg760) (GRID: grid.41156.37) (ISNI: 0000 0001 2314 964X) | |
| 700 | 1 | |a Li, Changhui |u Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China (ROR: https://ror.org/02v51f717) (GRID: grid.11135.37) (ISNI: 0000 0001 2256 9319); National Biomedical Imaging Center, Peking University, Beijing, China (ROR: https://ror.org/02v51f717) (GRID: grid.11135.37) (ISNI: 0000 0001 2256 9319) | |
| 773 | 0 | |t Nature Communications |g vol. 17, no. 1 (2026), p. 128-141 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
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