The Body Magnetic Resonance (BMR) Group develops and applies MRI to improve clinical care. We focus on breast MRI, abdominal MRI, MRI of osteoarthritis and MRI near metallic implants, with links between basic science, clinical imaging and industry. BMR is part of the Radiological Sciences Lab, Department of Radiology, School of Medicine, at Stanford University. Most of our students are in Electrical Engineering, Bioengineering, and Mechanical Engineering.
A Framework for Prospective Deployment of Deep Learning in MRI
Recent years have seen large advances in new artificial intelligence (AI) techniques for improving the current status of medical imaging, and specifically, magnetic resonance imaging (MRI).
Accelerated Multi-Shot DWI Reconstruction Using an Unrolled Network with U-Net as Priors
In this work, we accelerate and improve the reconstruction of multi-shot diffusion-weighted MRI by an unrolled pipeline, in which the presumed regularization term is replaced by a U-Net.
Diagnostic Accuracy of 5-Minute Knee MRI Using AI Image Quality Enhancement
Despite advances in accelerating MRI scans, diagnostic knee MRI protocols typically require upwards of 30 minutes of scanner time, which fundamentally limits patient throughput.
Multi-Shot DWI Reconstruction with Magnitude-Based Spatial-Angular Locally Low-Rank Regularization
Acquisition of diffusion-weighted images along multiple directions is needed for deriving microstructural metrics from diffusion models, and may also provide valuable information for some clinical applications.
Utility of Deep Learning Super-Resolution in the Context of Osteoarthritis MRI Biomarkers
Deep learning based super-resolution (SR) is a computer vision method that can enhance the resolution of low-resolution images, which has recently been applied to MRI.