Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N ​= ​9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, κ=0.72−0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: ε=1%−5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N ​= ​58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.

@en

To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N = 8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time.@en

The uptake mechanism of heavy metal ions in layered double hydroxides (LDHs) is investigated in this paper via solid-solution exchange experiments and first principle study. The uptake capacities of C-LDHs for heavy metal ions from solutions are experimentally investigated and the structures of LDHs doped with various heavy metal ions are revealed. The doped structures of LDHs are further re-established with first principle calculations. The results show that Cu²⁺ or Cr³⁺ ions are immobilized in the form of isomorphic substitution for Mg²⁺ and Al³⁺ in the plate of the layered structure, respectively, during reconstruction of calcined LDHs, forming a non-stoichiometric structure. The structure of the Cu²⁺ doped LDHs is identified as [Mg²⁺ _{(1−x)(1−z)}Cu²⁺ _(1−x)zAl³⁺ _x(OH)₂] Aⁿ⁻ _x/n·yH₂O, where z is the molar ratio of Cu²⁺ to Mg²⁺. The structure of Cr³⁺ doped LDHs is identified as [Mg²⁺ _1−x Cr³⁺ _xzAl³⁺ _x(1−z)(OH)₂] Aⁿ⁻ _(x+z)/n·yH₂O, where z is the molar ratio of Cr³⁺ to Al³⁺. The Cu²⁺ or Cr³⁺ ions in the hardened cement paste modified with calcined Mg-Al LDHs as immobilizing admixture can be efficiently removed from the pore solution and chemically stabilized in the structure of LDHs.

@en

The uptake mechanism of heavy metal ions in Layered double hydroxides (LDHs) is investigated in this paper via solid-solution exchange experiments and first principle study. The structures of heavy metal doped LDHs are revealed with XRD, TEM and solid state NMR analysis combined with first principle calculations. The experimental results show that Cu2+ or Cr3+ ions are immobilized in the form of isomorphic substitution during reconstruction of calcined LDHs, forming a non-stoichiometric structure. The Cu2+ or Cr3+ ions are immobilized via isomorphic substitution for Mg2+ and Al3+ in the plate of the layered structure, respectively. The structure of Cu2+ doped LDHs is identified as as [Mg2+ (1-x)(1- z)Cu2+ (1-x)zAl3+ x(OH)2] Anx/ n·yH2O, where z is the Cu2+/Mg2+ molar ratio. The structure of Cr3+ doped LDHs is identified as [Mg2+ 1-x Cr3+ xzAl3+ x(1-z)(OH)2] An- (x+z)/n·yH2O, where z is the Cr3+/Al3+ molar ratio. The Cu2+ or Cr3+ ions from mixing water can be efficiently immobilized with Mg-Al LDHs in cement.@en