Artificial intelligence accelerates tomography of fluorescent molecules
Zhang Guanglei, a team of researchers at the Beijing University of Aeronautics and Astronautics' Center for Biomedical Engineering, has applied artificial intelligence technology to fluorescent molecular tomography for the first time. It has significantly improved the target positioning accuracy and imaging contrast of the reconstruction results of fluorescent molecular tomography, and will reconstruct the speed. Increased by more than 1000 times. The research paper was recently published in the international journal Optics Express.
Fluorescence molecular tomography can observe the pathogenesis of cancer occurrence, development and metastasis at the molecular and cellular levels. It has the characteristics of non-invasive, in vivo and real-time monitoring, which is of great significance for the early diagnosis of cancer. However, problems such as lower imaging resolution and longer imaging time have prevented the technology from being used in biomedical research.
Based on the deep learning technology, Zhang Guanglei's team proposed an "end-to-end" reconstruction method based on 3D convolution depth encoder-decoder. This new method does not require a priori knowledge such as optical parameters. Instead, it continuously learns and adjusts model parameters through a large number of data mapping cases from internal fluorescence distribution to boundary fluorescence signal distribution, and directly establishes a nonlinear mapping relationship between input and output. "Deep learning based reconstruction methods do not require explicit definition of the forward and reverse problems of fluorescence molecular tomography reconstruction, fundamentally avoiding errors due to inaccuracies of linear models, and because there is no complexity in traditional reconstruction methods. The iterative calculation process greatly improves the reconstruction efficiency." Zhang Guanglei said. (Reporter Wang Yuyu)
Source: Health News Network
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