Deuterium ( 2 H) magnetic resonance imaging is an emerging approach for noninvasively studying glucose metabolism in vivo, which is important for understanding pathogenesis and monitoring the progression of many diseases such as tumors, diabetes, and neurodegenerative diseases. However, the synthesis of 2 H-labeled glucose is costly because of the expensive raw substrates and the requirement for extreme reaction conditions, making the 2 H-labeled glucose rather expensive and unaffordable for clinic use. In this study, we present a new deuterated compound, [2,3,4,6,6'-2 H 5 ]-D-glucose, with an approximate 10-fold reduction in production costs. The synthesis route uses cheaper raw substrate methyl-α-D-glucopyranoside, relies on mild reaction conditions (80 C), and has higher deuterium labeling efficiency. Magnetic resonance spectroscopy (MRS) and mass spectroscopy experiments confirmed the successful deuterium labeling in the compound. Animal studies demonstrated that the substrate could describe the glycolytic metabolism in a glioma rat model by quantifying the downstream metabolites through 2 H-MRS on an ultrahigh field system. Comparison of the glucose metabolism characteristics was carried out between [2,3,4,6,6'-2 H 5 ]-D-glucose and commercial [6,6'-2 H 2 ]-Dglucose in the animal studies. This cost-effective compound will help facilitate the clinical translation of deuterium magnetic resonance imaging, and enable this powerful metabolic imaging modality to be widely used in both preclinical and clinical research and applications.

An off-grid sparse direction-of-arrival (DOA) estimation algorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the DOA estimation precision present in unknown spatial coloured noise. The authors start by developing an off-grid sparse model based on linear interpolation with reweighted coefficient, which is a trade-off between tangent and secant offset, to guarantee an optimal approximation for off-grid signals. Next, the authors formulate the DOA estimation problem as solving the offgrid sparse model and, finally, the off-grid sparse model is addressed under the general framework of sparse Bayesian learning (SBL). Additional noise in IRLI is spatially coloured for calculating its statistical properties, which is different from SBL relying on the spatial white noise assumption. Numerical results with the limited snapshots and the low signal-to-noise ratio validate the algorithm by comparing with other algorithms.

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.