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Register a new userTailored nanodiamonds for hyperpolarized 13C MRI
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Nanodiamond is poised to become an attractive material for hyperpolarized 13C MRI if large nuclear polarizations can be achieved without the accompanying rapid spin-relaxation driven by paramagnetic species. Here we report enhanced and long-lived 13C polarization in synthetic nanodiamonds tailored by acid-cleaning and air-oxidation protocols. Our results separate the contributions of different paramagnetic species on the polarization behavior, identifying the importance of substitutional nitrogen defect centers in the nanodiamond core. These results are likely of use in the development of nanodiamond-based imaging agents with size distributions of relevance for examining biological processes.
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Monolithic integration of quantum emitters in nanoscale plasmonic circuitry requires low-loss plasmonic configurations capable of confining light well below the diffraction limit. We demonstrate on-chip remote excitation of nanodiamond-embedded single quantum emitters by plasmonic modes of dielectric ridges atop colloidal silver crystals. The nanodiamonds are produced to incorporate single germanium-vacancy (GeV) centers, providing bright, spectrally narrow and stable single-photon sources suitable for highly integrated circuits. Using electron-beam lithography with hydrogen silsesquioxane (HSQ) resist, dielectric-loaded surface plasmon polariton waveguides (DLSPPWs) are fabricated on single crystalline silver plates so as to contain those of spin-casted nanodiamonds that are found to feature appropriate single GeV centers. The low-loss plasmonic configuration enabled the 532 nm pump laser light to propagate on-chip in the DLSPPW and reach to an embedded nanodiamond where a single GeV center is incorporated. The remote GeV emitter is thereby excited and coupled to spatially confined DLSPPW modes with an outstanding figure-of-merit of 180 due to a ~6-fold Purcell enhancement, ~56% coupling efficiency and ~33 {mu}m transmission length, revealing the potential of our approach for on-chip realization of nanoscale functional quantum devices.
Here we report the size reduction and effects on nitrogen-vacancy centres in nanodiamonds by air oxidation using a combined atomic force and confocal microscope. The average height reduction of individual crystals as measured by atomic force microscopy was 10.6 nm/h at 600 {deg}C air oxidation at atmospheric pressure. The oxidation process modified the surface including removal of non-diamond carbon and organic material which also led to a decrease in background fluorescence. During the course of the nanodiamond size reduction, we observed the annihilation of nitrogen-vacancy centres which provided important insight into the formation of colour centres in small crystals. In these unirradiated samples, the smallest nanodiamond still hosting a stable nitrogen-vacancy centre observed was 8 nm.
Nitrogen-vacancy (NV) centers in diamonds are interesting due to their remarkable characteristics that are well suited to applications in quantum-information processing and magnetic field sensing, as well as representing stable fluorescent sources. Multiple NV centers in nanodiamonds (NDs) are especially useful as biological fluorophores due to their chemical neutrality, brightness and room-temperature photostability. Furthermore, NDs containing multiple NV centers also have potential in high-precision magnetic field and temperature sensing. Coupling NV centers to propagating surface plasmon polariton (SPP) modes gives a base for lab-on-a-chip sensing devices, allows enhanced fluorescence emission and collection which can further enhance the precision of NV-based sensors. Here, we investigate coupling of multiple NV centers in individual NDs to the SPP modes supported by silver surfaces protected by thin dielectric layers and by gold V-grooves (VGs) produced via the self-terminated silicon etching. In the first case, we concentrate on monitoring differences in fluorescence spectra obtained from a source ND, which is illuminated by a pump laser, and from a scattering ND illuminated only by the fluorescence-excited SPP radiation. In the second case, we observe changes in the average NV lifetime when the same ND is characterized outside and inside a VG. Fluorescence emission from the VG terminations is also observed, which confirms the NV coupling to the VG-supported SPP modes.
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov Chain Monte Carlo (MCMC) for the Bayesian model, and use the alternating direction method of multipliers (ADMM) for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
A to-date unsolved and highly limiting safety concern for Ultra High-Field (UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF) power in the body, quantified by the specific absorption rate (SAR), leading to dangerous tissue heating/damage in the form of local SAR hotspots that cannot currently be measured/monitored, thereby severely limiting the applicability of the technology for clinical practice and in regulatory approval. The goal of this study has been to show proof of concept of an artificial intelligence (AI) based exam-integrated real-time MRI safety prediction software (MRSaiFE) facilitating the safe generation of 3T and 7T images by means of accurate local SAR-monitoring at sub-W/kg levels. We trained the software with a small database of image as a feasibility study and achieved successful proof of concept for both field strengths. SAR patterns were predicted with a residual root mean squared error (RSME) of <11% along with a structural similarity (SSIM) level of >84% for both field strengths (3T and 7T).
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