Accelerated Phase Contrast MRI with Use of Resolution Enhancement Generative Adversarial Neural Network.

Background: Cardiovascular magnetic resonance (CMR) phase-contrast is used to quantify blood flow. We sought to develop a complex-difference reconstruction for inline super-resolution of phase-contrast (CRISPFlow) to accelerate phase-contrast imaging.

Methods: CRISPFlow was built on the super-resolution generative adversarial network.

Accelerated cardiac cine with spatio-coil regularized deep learning reconstruction.

Purpose: To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging.

Methods: This study proposes a Spatio-Coil Regularized DL (SCR-DL) approach for iterative deep learning reconstruction incorporating multicoil information in data consistency and regularizer. SCR-DL uses shift-invariant convolutional kernels to interpolate missing k-space lines and reconstruct individual coil images, followed by a regularizer that operates simultaneously across spatial and coil dimensions using learned image priors.

Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance.

Background: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR.

Methods: We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T.