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A-to-A embolic infarction has distinct vulnerable plaque characteristics compared with non-A-to-A embolic infarction. HIP and plaque surface irregularity may predict A-to-A embolic infarction.

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Purpose: Segmentation of multiple organs-at-risk (OARs) is essential for magnetic resonance (MR)only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra-and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency. Methods: We developed Automated deep Learning-based abdominal multiorgan segmentation (ALAMO) technique based on two-dimensional U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance. Results: The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within 1 min for a three-dimensional volume of 320 9 288 9 180. Overall, the ALAMO model matched the state-of-the-art techniques in performance. Conclusion: The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.

Water absorption behavior and mechanical properties of specimens of an epoxy resin casting and the glass fiber reinforced epoxy matrix composites (GFRP) with two fiber volume fractions ( Vf) immersed into artificial seawater were investigated. The composite specimens absorbed less water than the resin casting because water absorption by the matrices was the main way in the composites. The composites with a Vf of 44% absorbed more water than the composites with a Vf of 34% because there were more micropores inside the fiber bundles and capillary paths in the higher fiber volume fractions. Due to the reversible and irreversible changes in the resin casting and the failure of the fiber/matrix interface, the tensile strength, the flexural strength, and the ILSS of the composite specimens after 42 days’ immersion decreased 13%, 43%, 50%, respectively. And the tensile strength, the flexural strength and the ILSS of the specimens after desorption were 97%, 38%, and 43% of the original state, respectively.