The following are general descriptions of recent highlights from BMR publications. You may also view a complete list of BMR group publications.
Diffusion-Weighted DESS with a 3D Cones Trajectory for Non-Contrast-Enhanced Breast MRI
In this work a novel diffusion-weighted method, DW-DESS-Cones, is developed and characterized in vivo for the MRI of breast cancer without a contrast injection.
Multi-Shot DWI of the Breast with MUSE and shot-LLR Reconstructions
This manuscript presents a clinical breast MRI study that investigates the performance of single-shot DWI and multi-shot DWI reconstructed by two different techniques (MUSE and Shot-LLR).
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.
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.
Accelerated Imaging of Metallic Implants Using Model-Based Nonlinear Reconstruction
Total joint replacement is a common surgery to treat end-stage joint pain, which is projected to exceed 4.