Improving molecular imaging using a deep learning approach

Improving molecular imaging using a deep learning approach

Press releases may be edited for formatting or style | March 07, 2019 Artificial Intelligence Molecular Imaging

Yan developed this approach with corresponding author Xavier Intes, the other co-director of the Biomedical Imaging Center at Rensselaer, which is part of the Rensselaer Center for Biotechnology and Interdisciplinary Studies. Doctoral students Marien Ochoa and Ruoyang Yao supported the research.

“At the end, the goal is to translate these to a clinical setting. Usually when you have clinical systems you want to be as fast as possible,” said Ochoa, as she reflected on the speed with which this new technique allows researchers to capture these images.

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Further development is required before this groundbreaking new technology can be used in a clinical setting. However, its progress has been accelerated by incorporating simulated data based on modeling, a particular specialty for Intes and his lab.

“For deep learning usually you need a very large amount of data for training, but for this system we don’t have that luxury yet because it’s a very new system,” said Yan.

He said that the team’s research also shows that modeling can innovatively be used in imaging, accurately extending the model to the real experimental data.

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