During the 109th Scientific Assembly and Annual Meeting of RSNA 2023, Fang organized and hosted an educational session titled “Deep Learning in MRI“. The session featured two speakers who spoke about recent research and clinical updates on AI in MRI. Dr. Susie Huang from MGH/HST Martinos Center discussed the use of AI in rapid MRI, […]
Presentation
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Fang gave an educational talk at the 2022 RSNA
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
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Lab receives NIAMS R01 Grant for developing new quantitative MRI technology
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
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Congratulation! Lab is awarded NIH R01 Grant for Deep Learning and Rapid Imaging
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
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Top 10% most downloaded papers at Magnetic Resonance in Medicine
Four papers (MANTIS, SANTIS, SUSAN, Multi-Seg) from the team are among the 2018-2019 Top 10% most downloaded papers at Magnetic Resonance in Medicine.
Highlight
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Our paper on Artificial intelligence in MR image segmentation is Top20 most downloaded articles in the journal MRM
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.
Awards , Highlight
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Fang won the 1st Place Award for Oral Presentation at ISMRM Machine Learning Workshop
Fang won the 1st Place Award for Oral Presentation for the work entitled “MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for Efficient Estimation of MR Parameters” in the International Society for Magnetic Resonance in Medicine (ISMRM) 2018 Machine Learning Workshop II, Washington, D.C., USA.