Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.

Carbon nanotube (CNT) field emitters are now being evaluated for a wide range of vacuum electronic applications. However, problems including short lifetime at high current density, instability under high voltage, poor emission uniformity, and pixel-to-pixel inconsistency are still major obstacles for device applications. We developed an electrophoretic process to fabricate composite CNT films with controlled nanotube orientation and surface density, and enhanced adhesion. The cathodes have significantly enhanced macroscopic field emission current density and long-term stability under high operating voltages. The application of this CNT electron source for high-resolution x-ray imaging is demonstrated.

Current commercial micro-CT scanners have the capability of imaging objects ex vivo with high spatial resolution, but performing in vivo micro-CT on free-breathing small animals is still challenging because their physiological motions are non-periodic and much faster than those of humans. In this paper, we present a prototype physiologically gated micro-computed tomography (micro-CT) scanner based on a carbon nanotube field emission micro-focus x-ray source. The novel x-ray source allows x-ray pulses and imaging sequences to be readily synchronized and gated to non-periodic physiological signals from small animals. The system performance is evaluated using phantoms and sacrificed and anesthetized mice. Prospective respiratory-gated micro-CT images of anesthetized free-breathing mice were collected using this scanner at 50 ms temporal resolution and 6.2 lp mm(-1) at 10% system MTF. The high spatial and temporal resolutions of the micro-CT scanner make it well suited for high-resolution imaging of free-breathing small animals.

Morphology-based phylogenetic analyses support the monophyly of the Scalidophora (Kinorhyncha, Loricifera, Priapulida) and Nematoida (Nematoda, Nematomorpha), together constituting the monophyletic Cycloneuralia that is the sister group of the Panarthropoda. Kinorhynchs are unique among living cycloneuralians in having a segmented body with repeated cuticular plates, longitudinal muscles, dorsoventral muscles and ganglia. Molecular clock estimates suggest that kinorhynchs may have diverged in the Ediacaran Period. Remarkably, no kinorhynch fossils have been discovered, in sharp contrast to priapulids and loriciferans that are represented by numerous Cambrian fossils. Here we describe several early Cambrian (~535 million years old) kinorhynch-like fossils, including the new speciesEokinorhynchus rarusand two unnamed but related forms.E. rarushas characteristic scalidophoran features, including an introvert with pentaradially arranged hollow scalids. Its trunk bears at least 20 annuli each consisting of numerous small rectangular plates and is armored with five pairs of large and bilaterally placed sclerites. Its trunk annuli are reminiscent of the epidermis segments of kinorhynchs. A phylogenetic analysis resolvesE. rarusas a stem-group kinorhynch. Thus, the fossil record confirms that all three scalidophoran phyla diverged no later than the Cambrian Period.

In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction.