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Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known challenge. Recently, sparse representation-based techniques have been shown to be efficient in improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments conducted on phantom simulation data and clinical data. A comparison with previously reported solutions was also performed. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of LDCT image quality.

Although the quality of herbal medicine is tightly associated with plant genotype and location, microbial traits of most herbs remain unclear. In this study, bacterial communities residing Astragali Radix, which is derived from Astragalus membranaceus and A. mongholicus roots, have been characterized by automated ribosomal intergenic spacer analysis (ARISA) and pyrosequencing of 16S rRNA amplicons. The samples were collected from four representive locations and differing in genotype and planting pattern. The spatial resolution study by ARISA firstly distinguished between the two anatomically-based parts, the periderm and secondary vascular tissue, demonstrating that microbial communities residing in the former were more diverse and clearly separated by host genotype and location as compared with those residing in the latter. Taxonomic coverage revealed that phyla of Proteobacteria, Planctomycetes and Bacteroidetes dominated the bacterial assemblages across the samples. The community diversity in A. mongholicus was more abundant than it was in A. membranaceus, especially A. mongholicus in Shanxi province of China. In addition, the conventional cultivation exerted consistently negative effects on microbiota complexity when compared with the planting pattern “imitating wild conditions”, which was regardless of the host genotype. With the focus on microbiota members in Shanxi, taxa associated with the genotype, geography and planting pattern were finally discriminated and used as indicators for the screening of endophytic bacteria that contain ACC deaminase. Taken together, microbiota should be an important trait of herbal medicines and the periderm should be a specialized niche for microbiota research in medicinal plants.

International audience; Minimal path techniques can efficiently extract geometrically curve-like structures by finding the path with minimal accumulated cost between two given endpoints. Though having found wide practical applications (e.g., line identification, crack detection, and vascular centerline extraction), minimal path techniques suffer from some notable problems. The first one is that they require setting two endpoints for each line to be extracted (endpoint problem). The second one is that the connection might fail when the geodesic distance between the two points is much shorter than the desirable minimal path (shortcut problem). In addition, when connecting two distant points, the minimal path connection might become inefficient as the accumulated cost increases over the propagation and results in leakage into some non-feature regions near the starting point (accumulation problem). To address these problems, this paper proposes an approach termed minimal path propagation with backtracking. We found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance. The whole algorithm is robust to parameter setting and allows a coarse setting of the starting point. Extensive experiments with both simulated and realistic data are performed to validate the performance of the proposed method

Accurate prediction of CT image from MRI data is clinically desired for attenuation correction in PET/MR hybrid imaging systems and dose planning in MR-based radiation therapy. We present a k-nearest neighbor (KNN)-regression method to predict CT image from MRI data. In this method the nearest neighbors of each MR image patch are searched in the constraint spatial range. To improve the accuracy and efficiency of CT prediction, we propose to use of supervised descriptor learning based on low-rank approximation and manifold regularization to optimize the local descriptor of an MRimage patch and to reduce its dimensionality. The proposed method is evaluated on a dataset consisting of 13 subjects of paired brain MRI and CT images. Result shows that the proposed method can effectively predict CT images from MRI data and outperforms two state-of-the-art methods for CT prediction.