Hyperpolarized 129Xe MRI at low field: Current status and future directions

NMRduino: A modular, open-source, low-field magnetic resonance platform

The NMRduino is a compact, cost-effective, sub-MHz NMR spectrometer that utilizes readily available open-source hardware and software components. One of its aims is to simplify the processes of instrument setup and data acquisition control to make experimental NMR spectroscopy accessible to a broader audience. In this introductory paper, the key features and potential applications of NMRduino are described to highlight its versatility both for research and education.

Hyperpolarized Xenon-129 Chemical Exchange Saturation Transfer (HyperCEST) Molecular Imaging: Achievements and Future Challenges

Molecular magnetic resonance imaging (MRI) is an emerging field that is set to revolutionize our perspective of disease diagnosis, treatment efficacy monitoring, and precision medicine in full concordance with personalized medicine. A wide range of hyperpolarized (HP) 129Xe biosensors have been recently developed, demonstrating their potential applications in molecular settings, and achieving notable success within in vitro studies. The favorable nuclear magnetic resonance properties of 129Xe, coupled with its non-toxic nature, high solubility in biological tissues, and capacity to dissolve in blood and diffuse across membranes, highlight its superior role for applications in molecular MRI settings. The incorporation of reporters that combine signal enhancement from both hyperpolarized 129Xe and chemical exchange saturation transfer holds the potential to address the primary limitation of low sensitivity observed in conventional MRI. This review provides a summary of the various applications of HP 129Xe biosensors developed over the last decade, specifically highlighting their use in MRI. Moreover, this paper addresses the evolution of in vivo applications of HP 129Xe, discussing its potential transition into clinical settings.

Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review

This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods.