DTI Atlases Evaluations.

The cerebral atlas of diffusion tensor magnetic resonance image (DT-MRI, shorted as DTI) is one of the key issues in neuroimaging research. It is crucial for comparisons of neuronal structural integrity and connectivity across populations. Usually, the atlas is constructed by iteratively averaging the registered individual image. In tradition, the fuzzy group average image is easily generated in the initial stage, which is harmful to providing clear guidance for subsequent registration, to the performance of the final atlas. To solve this problem, an improved unbiased DTI atlas construction algorithm based on adaptive weights is proposed in this paper. The adaptive weighted strategy based on diffeomorphic deformable tensor registration is introduced. At the same time, the distance measure for tensors is used as a constraint condition, which ensures the unbiasedness of the atlas. Then, using 77 DTIs from the dataset in http://www.brain-development.org , three study-specific atlases, i.e. the constructed atlases of the proposed algorithm and two open-sourced algorithms (DTIAtlasBuilder and DTI-TK), are compared with two standardized atlases (IIT v. 4.1 and NTU-DSI-122-DTI). The performances of the atlases were evaluated in spatial normalization way with six region-based criteria (including Euclidean distances between diffusion tensors, Euclidean distances of the deviatoric tensors, standard deviation, overlaps of eigenvalue-eigenvector, cross-correlations and three sets angles of eigenvalue-eigenvector pairs between diffusion tensors) and three fiber-based criteria (including distances between fiber bundles, angles between fiber bundles and fiber property profile-based criteria). The experimental results showed that the overall performances of the study-specific atlases are better than those of the standardized atlases for specific datasets, and the comprehensive performance of the improved algorithm proposed in this paper is the best.

A comprehensive review of deep learning-based single image super-resolution.

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

Evaluations of diffusion tensor image registration based on fiber tractography.

BACKGROUND: Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development. METHODS AND RESULTS: In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template. CONCLUSION: All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it.

[China's rice field greenhouse gas emission under climate change based on DNDC model simulation].

In contrast to a large body of literature assessing the impact of agriculture greenhouse gas (GHG) emissions on climate change, there is a lack of research examining the impact of climate change on agricultural GHG emissions. This study employed the DNDC v9.5, a state-of-art biogeochemical model, to simulate greenhouse gas emissions in China' s rice-growing fields during 1971-2010. The results showed that owing to temperature rising (on average 0.49 °C higher in the second 20 years than in the first 20 year) and precipitation increase (11 mm more in the second 20 years than in the first 20 years) during the rice growing season, CH4 and N2O emissions in paddy field increased by 0.25 kg C . hm-2 and 0.25 kg N . hm-2, respectively. The rising temperature accelerated CH4 emission and N2O emission increased with precipitation. These results indicated that climate change exerted impact on the mechanism of GHG emissions in paddy field.