Hyperpolarized xenon 129 MRI and total lung diffusion capacity for carbon monoxide demonstrate significantly impaired gas transfer in non-hospitalized post-COVID-19 condition participants with norm... Background Post-Covid-19 condition describes symptoms following COVID-19 infection after four weeks. Symptoms are wide-ranging but breathlessness is common. Purpose The purpose of this study was to...

Carbon 13 (13C) MRI following injection of hyperpolarized 13C-labeled pyruvate can be used to characterize glioblastoma metabolism and changes in the surrounding brain parenchyma, and this tumor me... Purpose To evaluate glioblastoma (GBM) metabolism by using hyperpolarized carbon 13 (13C) MRI to monitor the exchange of the hyperpolarized 13C label between injected [1-13C]pyruvate and tumor lact...

This review provides a comprehensive assessment of the development of hyperpolarized (HP) carbon-13 metabolic MRI from the early days to the present with a focus on clinical applications. The status and upcoming challenges of translating HP carbon-13 into clinical application are reviewed, along with the complexity, technical advancements, and future directions. The road to clinical application is discussed regarding clinical needs and technological advancements, highlighting the most recent successes of metabolic imaging with hyperpolarized carbon-13 MRI. Given the current state of hyperpolarized carbon-13 MRI, the conclusion of this review is that the workflow for hyperpolarized carbon-13 MRI is the limiting factor.

Purpose: To develop and optimize a rapid dynamic hyperpolarized ¹²⁹Xe ventilation (DXeV) MRI protocol and investigate the feasibility of capturing pulmonary signal‐time curves in human lungs.Theory and Methods: Spiral k‐space trajectories were designed with the number of interleaves N_int = 1, 2, 4, and 8 corresponding to voxel sizes of 8 mm, 5 mm, 4 mm, and 2.5 mm, respectively, for field of view = 15 cm. DXeV images were acquired from a gas‐flow phantom to investigate the ability of N_int = 1, 2, 4, and 8 to capture signal‐time curves. A finite element model was constructed to investigate gas‐flow dynamics corroborating the experimental signal‐time curves. DXeV images were also carried out in six subjects (three healthy and three chronic obstructive pulmonary disease subjects).Results: DXeV images and numerical modelling of signal‐time curves permitted the quantification of temporal and spatial resolutions for different numbers of spiral interleaves. The two‐interleaved spiral (N_int = 2) was found to be the most time‐efficient to obtain DXeV images and signal‐time curves of whole lungs with a temporal resolution of 624 ms for 13 slices. Signal‐time curves were well matched in three healthy volunteers. The Spearman's correlations of chronic obstructive pulmonary disease subjects were statistically different from three healthy subjects (P < 0.05).Conclusion: The N_int = 2 spiral demonstrates the successful acquisition of DXeV images and signal‐time curves in healthy subjects and chronic obstructive pulmonary disease patients. Magn Reson Med 79:2597–2606, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multi-slice free breathing and ungated cardiac MRI data from highly undersampled measurements. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions.

In the metabolism of acetate several enzymes are involved, which play an important role in free fatty acid oxidation. Fatty acid metabolism is altered in diabetes patients and therefore acetate might serve as a marker for pathological changes in the fuel selection of cells, as these changes occur in diabetes patients. Acetylcarnitine is a metabolic product of acetate, which enables its transport into the mitochondria for energy production. This study investigates whether the ratio of acetylcarnitine to acetate, measured by noninvasive hyperpolarized [1‐13C]acetate magnetic resonance spectroscopy, could serve as a marker for myocardial, hepatic, and renal metabolic changes in rats with Streptozotocin (STZ)‐induced diabetes in vivo. We demonstrate that the conversion of acetate to acetylcarnitine could be detected and quantified in all three organs of interest. More interestingly, we found that the hyperpolarized acetylcarnitine to acetate ratio was independent of blood glucose levels and prolonged hyperglycemia following diabetes induction in a type‐1 diabetes model.