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The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. Multiple deep learning-based structures have been proposed for MRI reconstruction using CS, in both the k-space and image domains, and using unrolled optimization methods. However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image). Herein, we propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domains. We evaluate our method on a well-known open-source MRI dataset (652 brain cases and 1172 knee cases) and a clinical MRI dataset of 243 patients diagnosed with strokes from our institution to demonstrate the performance of our network. Our model achieves an overall peak signal-to-noise ratio/structural similarity of 40.92 ± 0.29/0.9577 ± 0.0025 (fourfold) and 37.03 ± 0.25/0.9365 ± 0.0029 (eightfold) for the brain dataset, 31.09 ± 0.25/0.6901 ± 0.0094 (fourfold) and 29.49 ± 0.22/0.6197 ± 0.0106 (eightfold) for the knee dataset, and 36.32 ± 0.16/0.9199 ± 0.0029 (20-fold) and 33.70 ± 0.15/0.8882 ± 0.0035 (30-fold) for the stroke dataset. In addition to quantitative evaluation, we undertook a blinded comparison of image quality across networks performed by a subspecialty trained radiologist. Overall, we demonstrate that our network achieves a superior performance among others under multiple reconstruction tasks. 

Abstract Diatom frustules are a type of porous silicon dioxide microparticle that has long been used in applications ranging from biomedical sensors to dye‐sensitized solar cells. The favorable material properties, enormous surface area, and enhanced light scattering capacity support the promise of diatom frustules as candidates for next generation biomedical devices and energy applications. In this study, the vapor–liquid–solid (VLS) method is employed to incorporate silica nanowires on the surface of diatom frustules. Compared to the original frustule structures, the frustule–nanowire composite material's surface area increases over 3‐fold, and the light scattering ability increases by 10%. By varying the gold catalyst thickness during the VLS process, tuning of the resultant nanowire length/density is achieved. Through material characterization, it is determined that both float growth and root growth processes jointly result in the growth of the silica nanowires. From a thermodynamics point of view, the preferential growth of the silica nanowires on frustules is found to have resulted from the enormous partial surface area of gold nanoparticles on the diatom frustules. The frustule–nanowire composite materials have potential applications in the development of novel biomedical sensing devices and may greatly enhance next generation solar cell performance. 

Abstract Diatoms are photosynthetic algae that exist ubiquitously throughout the planet in water environments. Over the preceding decades, the diatom exoskeletons, termed frustules, featuring abundant micro‐ and nanopores, have served as the source material and inspiration for myriad research efforts. In this work, it is demonstrated that frustule‐inspired hierarchical nanostructure designs may be utilized in the fabrication of metamaterial absorbers, thereby realizing a broadband infrared (IR) absorber with excellent performance in terms of absorption. In an effort to investigate the origin of this absorption characteristic, numerical models are developed to study these structures, revealing that the hierarchical organization of the constituent nanoparticulate metamaterial unit cells introduce an additional resonance mode to the device, broadening the absorption spectrum. It is further demonstrated that the resonant peaks shift linearly as a function of inter‐unit‐cell spacing in the metamaterial, which is attributed to the induced collective dipole mode by the nanoparticles. Ultimately, the work herein represents an innovative perspective in terms of the design and fabrication of IR absorbers inspired by naturally occurring biomaterials, offering the potential to lead to advances in metamaterial absorber technology.