Metrics of image quality show that abnormalities—including pathologies—deteriorate more than do normal-appearing areas on brain MRIs reconstructed with deep learning models at acceleration factors of six and up.
Tag: Image Reconstruction
Imaging researchers at NYU Langone have used deep learning to turn noise against itself in order to improve low-field MRI. They’re after something much bigger than sharper images.
Congratulations to Ruoxun Zi on a successful defense of her doctoral dissertation in biomedical imaging and technology at NYU Grossman School of Medicine.
The fastMRI dataset now includes curated breast MRI data to boost AI innovation in radial, dynamic contrast-enhanced, ultrafast MRI of the breast.
Since 2014, the Center for Advanced Imaging Innovation and Research has been in the lead of technological shifts in magnetic resonance imaging. An award from the National Institutes of Health is extending the center’s mandate for a third five-year term.
Marcelo Zibetti, imaging scientist at NYU Langone Health, talks about efficiency in MRI, the value of differing vantage points, and learning by thinking across disciplines.
Radhika Tibrewala, graduate student in biomedical imaging, talks about the new fastMRI prostate dataset, deep learning in MRI, and how she started a PhD remotely in 2020.
Li Feng, developer of fast MRI techniques, talks about going beyond speed, his path to academia, and the rewards of persistence.
Patricia Johnson, who researches machine learning image reconstruction, talks about faster MRI, visual preferences, and diagnostic interchangeability.
MR images reconstructed from undersampled scans with AI have similar diagnostic value as those reconstructed with conventional methods, find scientists at NYU Langone and Meta AI Research.