We report magnetic and transport properties of La 1Ϫx Ba x MnO 3 (xϭ0.05-0.33) epitaxial thin films. Compared with the corresponding bulk materials, the ferromagnetic transition temperature is reduced in the compressive strained La 1Ϫx Ba x MnO 3 thin films with xϭ0.3 and 0.33, but enhanced significantly in the tensile strained thin films with xр0.2. Especially, ferromagnetism and low field colossal magnetoresistance effect were observed around room temperature in xϭ0.1 thin film, and as xϭ0.05, a spin-canting insulating state in bulk shifts to ferromagnetic metallic state in thin film. The phase diagram of La 1Ϫx Ba x MnO 3 thin films was obtained, and strain effect on these novel properties was discussed.
We report on the electrical modulation of double exchange ferromagnetism at room temperature in hole-doped manganites of a metal oxide p-n junction. In this (La0.9Ba0.1)MnO(3)/Nb doped SrTiO3 p-n junction, the temperature dependence of the junction resistance shows a metal-insulator transition whose temperature, corresponding to that of ferromagnetic transition, is hugely modulated from 290 to 340 K by a bias voltage increasing from +1.0 to +1.8 V. The magnetoresistance can also be modulated electrically.
Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is challenging and time-consuming for manual delineation of PVSs. Although several automatic/semi-automatic methods, especially the traditional learning-based approaches, have been proposed for segmentation of 3D PVSs, their performance often depends on the hand-crafted image features, as well as sophisticated preprocessing operations prior to segmentation (e.g., specially defined regions-of-interest (ROIs)). In this paper, a novel fully convolutional neural network (FCN) with no requirement of any specified hand-crafted features and ROIs is proposed for efficient segmentation of PVSs. Particularly, the original T2-weighted 7T magnetic resonance (MR) images are first filtered via a non-local Haar-transform-based line singularity representation method to enhance the thin tubular structures. Both the original and enhanced MR images are used as multi-channel inputs to complementarily provide detailed image information and enhanced tubular structural information for the localization of PVSs. Multi-scale features are then automatically learned to characterize the spatial associations between PVSs and adjacent brain tissues. Finally, the produced PVS probability maps are recursively loaded into the network as an additional channel of inputs to provide the auxiliary contextual information for further refining the segmentation results. The proposed multi-channel multi-scale FCN has been evaluated on the 7T brain MR images scanned from 20 subjects. The experimental results show its superior performance compared with several state-of-the-art methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.