Purpose: In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distribution function (fODF) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultimately facilitates the understanding of brain connectivity and exploration of neurological dysfunction. Recently, multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) method has been explored for reconstructing full fODFs. To achieve a reliable fitting, similar to other model-based approaches, a large number of diffusion measurements is typically required for MSMT-CSD method. The prolonged acquisition is, however, not feasible in practical clinical routine and is prone to motion artifacts. To accelerate the acquisition, we proposed a method to reconstruct the fODF from downsampled diffusion-weighted images (DWIs) by leveraging the strong inference ability of the deep convolutional neural network (CNN). Methods: The method treats spherical harmonics (SH)-represented DWI signals and fODF coefficients as inputs and outputs, respectively. To compensate for the reduced gradient directions with reduced number of DWIs in acquisition in each voxel, its surrounding voxels are incorporated by the network for exploiting their spatial continuity. The resulting fODF coefficients are fitted with applying the CNN in a multi-target regression model. The network is composed of two convolutional layers and three fully connected layers. To obtain an initial evaluation of the method, we quantitatively measured its performance on a simulated dataset. Then, for in vivo tests, we employed data from 24 subjects from the Human Connectome Project (HCP) as training set and six subjects as test set. The performance of the proposed method was primarily compared to the super-resolved MSMT-CSD with the decreasing number of DWIs. The fODFs reconstructed by MSMT-CSD from all available 288 DWIs were used as training labels and the reference standard. The performance was quantitatively measured by the angular correlation coefficient (ACC) and the mean angular error (MAE). Results: For the simulated dataset, the proposed method exhibited the potential advantage over the model reconstruction. For the in vivo dataset, it achieved superior results over the MSMT-CSD in all the investigated cases, with its advantage more obvious when a limited number of DWIs were used. As the number of DWIs was reduced from 95 to 25, the median ACC ranged from 0.96 to 0.91 for the CNN, but 0.93 to 0.77 for the MSMT-CSD (with perfect score of 1). The angular error in the typical regions of interest (ROIs) was also much lower, especially in multi-fiber regions. The average MAE for the CNN method in regions containing one, two, three fibers was, respectively, 1.09°, 2.75°, and 8.35°smaller than the MSMT-CSD method. The visual inception of the fODF further 3101 confirmed this superiority. Moreover, the tractography results validated the effectiveness of the learned fODF, in preserving known major branching fiber...

Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.

Diffusion kurtosis imaging (DKI) is an advanced diffusion imaging method that captures complex brain microstructural properties; however, it often has a lengthy acquisition time compared to conventional diffusion tensor imaging (DTI). Recently, a deep learning-based method has shown the potential for reducing the number of diffusion-weighted images (DWIs) required to compute the rotationally invariant scalar measures to twelve. In this study, we propose a three-dimensional (3D) convolutional neural network (CNN) to estimate the scalar measures. This network further improves the performance of the deep learning-based method with a largely reduced number of required DWIs. In our approach, all the DTI and DKI measures were estimated using a single network, and a hierarchical structure was introduced to customize the outputs based on their computational complexities and to learn the commonalities of the measures. Moreover, 3 × 3 × 3 convolution kernels were introduced to extract features from the 3D input patches and utilize the spatial context from adjacent neighborhoods, which also strengthened the network's robustness against noise. The proposed method was evaluated with two datasets. The results showed that, compared with the previous method that used an artificial neural network, our proposed hierarchical CNN provided enhanced efficiency for estimating all eight diffusion measures. It also improved the robustness against noise and retained the fine structures with only a few DWIs (as few as eight). This result suggests that it is possible to achieve kurtosis mapping in most clinical scanners within one minute, which could significantly extend the clinical utility of the DKI.

Carrier-free 209Po solution standards have been prepared and calibrated. The standards, which will be disseminated by the National Institute of Standards and Technology as Standard Reference Material SRM 4326, consist of (5.1597 ±0.0024) g of a solution of polonium in nominal 2 mol · L−1 hydrochloric acid (having a solution density of (1.031±0.004) g · mL−1 at 22 °C) that is contained in 5 mL flame-sealed borosilicate glass ampoules, and are certified to contain a 209Po alpha-particle emission rate concentration of (85.42±0.29) s−1 · g−1 (corresponding to a 209Po activity concentration of (85.83 ±0.30) Bq · g−1) as of the reference time of 1200 EST 15 March 1994. The calibration was based on 4πα liquid scintillation (LS) measurements with two different LS counting systems and under wide variations in measurement and sample conditions. Confirmatory measurements by 2πα gas-flow proportional counting were also performed. The only known radionuclidic impurity, based on α- and photon-emission spectrometry, is a trace quantity of 208Po. The 208Po to 209Po impurity ratio as of the reference time was 0.00124 ±0.00020. All of the above cited uncertainty intervals correspond to a combined standard uncertainty multiplied by a coverage factor of k = 2. Although 209Po is nearly a pure α emitter with only a weak electron capture branch to 209Bi, LS measurements of the 209Po a decay are confounded by an a transition to a 2.3 keV (Jπ= 1/2−) level in 205Pb which was previously unknown to be a delayed isomeric state.

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