Abstract: A method for magnetic resonance imaging corrects non-stationary off-resonance image artifacts. A magnetic resonance imaging (MRI) apparatus performs an imaging acquisition using non-Cartesian trajectories and processes the imaging acquisitions to produce a final image. The processing includes reconstructing a complex-valued image and using a convolutional neural network (CNN) to correct for non-stationary off-resonance artifacts in the image. The CNN is preferably a residual network with multiple residual layers.

Abstract: A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.

Abstract: A magnetic resonance imaging (MRI) techniques uses a T2-preparation outer volume suppression (OVS) pulse sequence to reduce the longitudinal magnetization outside a region of interest. A region is excited that includes the region of interest, radiofrequency (RF) signals are detected, and MRI images generated from the RF detected signals. The T2-preparation OVS pulse sequence includes, sequentially: a first tip-down excitation pulse, a first refocusing excitation pulse, a first tip-up excitation pulse that is selective spatially and/or spectrally, a second tip-down excitation pulse that is 180° out of phase with respect to the first tip-down excitation pulse, a second refocusing excitation pulse, and a second tip-up excitation pulse that is selective spatially and/or spectrally.

Abstract: A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.

Abstract: A method for magnetic resonance imaging corrects non-stationary off-resonance image artifacts. A magnetic resonance imaging (MRI) apparatus performs an imaging acquisition using non-Cartesian trajectories and processes the imaging acquisitions to produce a final image. The processing includes reconstructing a complex-valued image and using a convolutional neural network (CNN) to correct for non-stationary off-resonance artifacts in the image. The CNN is preferably a residual network with multiple residual layers.

Abstract: A magnetic resonance imaging (MRI) techniques uses a T2-preparation outer volume suppression (OVS) pulse sequence to reduce the longitudinal magnetization outside a region of interest. A region is excited that includes the region of interest, radiofrequency (RF) signals are detected, and MRI images generated from the RF detected signals. The T2-preparation OVS pulse sequence includes, sequentially: a first tip-down excitation pulse, a first refocusing excitation pulse, a first tip-up excitation pulse that is selective spatially and/or spectrally, a second tip-down excitation pulse that is 180° out of phase with respect to the first tip-down excitation pulse, a second refocusing excitation pulse, and a second tip-up excitation pulse that is selective spatially and/or spectrally.