Presentation

Fang gave an educational talk at the 2022 RSNA

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In the 108th Scientific Assembly and Annual Meeting of RSNA 2022, Fang was invited to organize and host an educational session entitled “Deep Learning in MRI.” He also delivered a presentation entitled “Deep Learning Reconstruction for Improving (Accelerating) MRI and MRI-Based Tissue Quantification,” detailing fundamental MRI physics and image formulation and explaining how modern AI […]

Funding , Highlight

Lab receives NIAMS R01 Grant for developing new quantitative MRI technology

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We are thrilled to receive another 5-year R01 grant from The National Institutes of Health and National Institute of Arthritis and Musculoskeletal and Skin Disease (NIAMS). This new funding will support our lab to develop cutting-edge Artificial Intelligence and Machine Learning methods to improve MRI and image-based non-invasive human tissue quantification, particularly through ultra-high-resolution multi-dimensional quantitative imaging. We aim to explore, implement and optimize a […]

Funding , Highlight

Congratulation! Lab is awarded NIH R01 Grant for Deep Learning and Rapid Imaging

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We are delighted to receive a new 5-year R01 grant from The National Institutes of Health and the National Institute of Arthritis and Musculoskeletal and Skin Disease (NIAMS). This new funding will support our lab at Harvard Medical School and Massachusetts General Hospital to explore a fundamentally new approach to advancing musculoskeletal MRI in all aspects, including rapid image acquisition, reconstruction, advanced image analysis […]

Highlight

Our paper on Artificial intelligence in MR image segmentation is Top20 most downloaded articles in the journal MRM

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Our paper on artificial intelligence method in MR image segmentation and modeling entitled “Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging” is one of the Top20 downloaded articles in Magnetic Resonance in Medicine between 2017 and 2018.