We are thrilled to receive a 1-year R56 bridge 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 a multi-parametric imaging method capable of providing rapid, accurate, and efficient non-invasive imaging biomarkers to assess knee joint degeneration in knee osteoarthritis. This […]
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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 […]
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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 […]
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Lab receives new Trailblazer R21 Award from NIBIB
We are thrilled to receive a new 3-year Trailblazer R21 Grant entitled “Deep Learning Reconstruction for Rapid Multi-Component Relaxometry” from The National Institutes of Health and the National Institute of Biomedical Imaging and Bioengineering (NIBIB). This exciting project will develop novel Artificial Intelligence and Machine Learning methods to improve MRI speed, accuracy, and efficiency in non-invasively imaging human diseases and quantifying human tissue microstructures […]
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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.
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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.
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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.
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Our paper is selected as Editor’s Pick in Magnetic Resonance in Medicine
The paper entitled “Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging” is selected as May 2018 Editor’s Pick in Magnetic Resonance in Medicine.