DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution.

Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical structures and more precise clinical information than thick-section images. However, thin-section MRI is not always available due to the imaging cost issue. In multicenter retrospective studies, a large number of data are often in thick-section manner with different section thickness. The lack of thin-section data and the difference in section thickness bring considerable difficulties in the study based on the image big data. In this article, we introduce DeepVolume, a two-step deep learning architecture to address the challenge of accurate thin-section MR image reconstruction. The first stage is the brain structure-aware network, in which the thick-section MR images in axial and sagittal planes are fused by a multitask 3-D U-net with prior knowledge of brain volume segmentation, which encourages the reconstruction result to have correct brain structure. The second stage is the spatial connection-aware network, in which the preliminary reconstruction results are adjusted slice-by-slice by a recurrent convolutional network embedding convolutional long short-term memory (LSTM) block, which enhances the precision of the reconstruction by utilizing the previously unassessed sagittal information. We used 305 paired brain MRI samples with thickness of 1.0 mm and 6.5 mm in this article. Extensive experiments illustrate that DeepVolume can produce the state-of-the-art reconstruction results by embedding more anatomical knowledge. Furthermore, considering DeepVolume as an intermediate step, the practical and clinical value of our method is validated by applying the brain volume estimation and voxel-based morphometry. The results show that DeepVolume can provide much more reliable brain volume estimation in the normalized space based on the thick-section MR images compared with the traditional solutions.

The Lightweight Design of a Seismic Low-Yield-Strength Steel Shear Panel Damper.

The lightweight design and miniaturization of metallic dampers have broad application prospects in seismic engineering. In this study, the superplastic property and the maximum energy dissipation capacity per unit mass of low-yield-strength steel (LYS) are investigated via comparison with those of several common metallic damping materials by tests. Additionally, the boundary constraints of an LYS shear panel damper are studied further. Our experimental results suggest that LYS is an excellent damping material for achieving the lightweight design goal. A novel design of a lightweight damper, having excellent deformation ability and robust mechanical properties, is presented. The findings of this study are expected to be useful in understanding the lightweight design of dampers.