Fluorite-structure binary oxides (e.g., HfO 2 and ZrO 2 ) have attracted increasing interest for a broad range of applications including thermal barrier coatings, high-k dielectrics, and novel ferroelectrics. A crystalline structure plays a crucial role in determining physical and chemical properties. Structure evolution of ZrO 2 thin films, particularly down to the nanometer scale, has not been thoroughly studied. In this work, we carried out systematic annealing analysis on the ZrO 2 thin films. Through in-situ high-temperature X-ray diffraction (XRD) characterizations, a thickness dependence of crystallization and phase transition is observed. Irrespective of the thickness (10-300 nm), the as-prepared amorphous ZrO 2 thin films are preferentially crystallized into a tetragonal (t) structure (high-temperature phase), which can be preserved down to room temperature (RT) upon annealing at the corresponding crystallization temperature (T C ). When annealing at temperatures higher than T C , the transition from t to monoclinic (m; RT phase) will occur, and the quantity of the transition strongly depends on the film thickness. Our work expands the basic understanding of the phase transition in the ZrO 2 thin films, and offers a path to the selective control over the phase structure for novel functionalities.
BackgroundAccurate delineation of the midbrain nuclei, the red nucleus (RN), substantia nigra (SN) and subthalamic nucleus (STN), is important in neuroimaging studies of neurodegenerative and other diseases. This study aims to segment midbrain structures in high-resolution susceptibility maps using a method based on a convolutional neural network (CNN).MethodsThe susceptibility maps of 75 subjects were acquired with a voxel size of 0.83 × 0.83 × 0.80 mm3 on a 3T MRI system to distinguish the RN, SN, and STN. A deeply supervised attention U-net was pre-trained with a dataset of 100 subjects containing susceptibility maps with a voxel size of 0.63 × 0.63 × 2.00 mm3 to provide initial weights for the target network. Five-fold cross-validation over the training cohort was used for all the models’ training and selection. The same test cohort was used for the final evaluation of all the models. Dice coefficients were used to assess spatial overlap agreement between manual delineations (ground truth) and automated segmentation. Volume and magnetic susceptibility values in the nuclei extracted with automated CNN delineation were compared to those extracted by manual tracing. Consistencies of volume and magnetic susceptibility values by different extraction strategies were assessed by Pearson correlation coefficients and Bland-Altman analyses.ResultsThe automated CNN segmentation method achieved mean Dice scores of 0.903, 0.864, and 0.777 for the RN, SN, and STN, respectively. There were no significant differences between the achieved Dice scores and the inter-rater Dice scores (p > 0.05 for each nucleus). The overall volume and magnetic susceptibility values of the nuclei extracted by the automatic CNN method were significantly correlated with those by manual delineation (p < 0.01).ConclusionMidbrain structures can be precisely segmented in high-resolution susceptibility maps using a CNN-based method.
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