CYCLE-CONSISTENT SELF-SUPERVISED LEARNING FOR IMPROVED HIGHLY-ACCELERATED MRI RECONSTRUCTION.

Physics-driven deep learning (PD-DL) has become a powerful tool for accelerated MRI. Recent developments have also developed unsupervised learning for PD-DL, including self-supervised learning. However, at very high acceleration rates, such approaches show performance deterioration.

TOWARDS FAST HARD-CONSTRAINED PARALLEL TRANSMIT DESIGN IN ULTRAHIGH FIELD MRI WITH PHYSICS-DRIVEN NEURAL NETWORKS.

Parallel transmission (pTx) is an important technique for reducing transmit field inhomogeneities at ultrahigh-field (UHF) MRI. pTx typically involves solving an optimization problem for radiofrequency pulse design, with hard constraints on specific-absorption rate (SAR) and/or power, which may be time-consuming. In this work, we propose a novel approach towards incorporating hard constraints to physics-driven neural networks.

The effect of art therapy on cognitive status and psychological well-being in elderly people in institutional care.

Background: Cognitive decline seen in old age manifests itself as a decrease in reasoning ability that is not related to intelligence. This situation, together with many other problems, can affect the psychological well-being of the elderly. Especially the elderly living in institutional care constitute a special group that needs to be protected.

Accelerated phase-contrast magnetic resonance imaging 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 flow (CRISPFlow) to accelerate phase-contrast imaging.

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

Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction.