PURPOSE: Simultaneous multi-slice acquisitions are essential for modern neuroimaging research, enabling high temporal resolution functional and high-resolution q-space sampling diffusion acquisitions. Recently, deep learning reconstruction techniques...
PURPOSE: To design a new deep learning network for fast and accurate water-fat separation by exploring the correlations between multiple echoes in multi-echo gradient-recalled echo (mGRE) sequence and evaluate the generalization capabilities of the n...
PURPOSE: The purpose of this study is to demonstrate a method for specific absorption rate (SAR) reduction for 2D T -FLAIR MRI sequences at 7 T by predicting the required adiabatic radiofrequency (RF) pulse power and scaling the RF amplitude in a sli...
PURPOSE: To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging.
PURPOSE: To develop a deep learning method for rapidly reconstructing T and T maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images.
PURPOSE: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training.
PURPOSE: We aim to leverage the power of deep-learning with high-fidelity training data to improve the reliability and processing speed of hemodynamic mapping with MR fingerprinting (MRF) arterial spin labeling (ASL).
PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects.