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Follow on Google News | Faster holographic imaging using recurrent neural networksBy: UCLA ITA A research team at UCLA has recently developed a novel holographic phase retrieval technique that can rapidly reconstruct microscopic images of samples with up to 50-fold acceleration compared to existing methods. This new technique is taking advantage of recurrent neural networks trained using deep learning and incorporates spatial features from multiple holograms to digitally create holographic microscopy images of samples, such as human tissue slides. This results in better image quality and faster reconstruction speed, while also enhancing the depth-of-field of the reconstructed sample volume. This work was published in ACS Photonics (https://pubs.acs.org/ "This framework can be broadly applicable to various biomedical imaging modalities, including for example fluorescence microscopy, to efficiently utilize a sequence of acquired images to rapidly and accurately create 3D reconstructions of a sample volume," said Dr. Aydogan Ozcan, the Chancellor's Professor of Electrical and Computer Engineering at UCLA and an associate director of the California NanoSystems Institute, who is the senior corresponding author of the work. The other authors include graduate students Luzhe Huang, Tairan Liu, Xilin Yang, Yi Luo and Professor Yair Rivenson, all from the Electrical and Computer Engineering department at UCLA. Professor Ozcan also has UCLA faculty appointments in bioengineering and surgery, and is an HHMI professor. Link to paper: https://pubs.acs.org/ End
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