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).
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.
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.
Optimization of Quantitative Sequences Using Automatic Differentiation of Bloch Simulations
We apply automatic differentiation (widely used in deep learning) and a simple Bloch simulation to efficiently optimize any sequence that can be simulated.
5-Minute Quantitative Double-Echo in Steady-State Sequence for Comprehensive Whole-Joint Knee MRI
Clinical knee MRI examinations typically utilize 2D imaging methods with thick slices, requiring upwards of 20-25 minutes of scan time, and yet not producing any quantitative information.