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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.

International audience; –Low-dose CT (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)". In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation (DSR) operation based on these dictionaries. Then, a general dictionary learning (DL) processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems. Index Terms—Low-dose CT (LDCT), dictionary learning, noise, artifact suppression, artifact suppressed dictionary learning algorithm (ASDL)

The Web Access to Digital Imaging and Communication in Medicine (DICOM) Persistent Objects (WADO) standard specifies a Web-based service for accessing and presenting DICOM persistent objects, such as images and medical imaging reports. This paper analyzes the image transferring mechanism of the WADO service and proposes to extend its abilities by accessing 16-bit images and incorporating a denoising method clinically in demand. In our Electronic Health Record (EHR) implementations, we found that due to severe degradation by quantum noise and artifacts under low dose scan protocols, direct display of low-dose CT (LDCT) images tends not to be clinically desired. We propose to improve LDCT images in web clients of the EHR without downloading anything except HTML codes. We achieved that by using advanced features of the new HTML5 standard. Our implementation is plug-in-less, ensuring to enhance LDCT images anywhere, anytime in any HTML5 compliant EHR client. Evaluation results validated our improvements of the WADO service in enhancing clinical LDCT images.

Purpose Though highly desirable in radiologic procedures, low-dose CT (LDCT) images tend to be severely degraded by quantum noise and non-stationary artifacts. The purpose of this paper is to improve the abdominal LDCT images by the approach of Weighted Intensity Averaging over Large-scale Neighborhoods (WIA-LN). Materials and methods In the implementation of the proposed WIA-LN method, the processed pixel intensities are adaptively calculated as the weighted intensity averaging of the pixels with similar surrounding structures throughout a large-scale neighborhood. Both phantom and clinical abdominal CT images from a 16 detector rows Siemens CT were acquired at standard and 80% reduced tube current time products (150 mA s and 30 mA s corresponding to standard-dose and low-dose protocols, respectively). Visual comparison, statistical qualitative analysis (image quality scores and hepatic cyst diagnosis), and quantitative calculation (noise and contrast-to-noise ratio) are made. Results Better vision and quantitative performance are realized using the proposed WIA-LN method. Compared to original LDCT and standard-dose CT (SDCT) images, statistically significant improvement of noise/artifacts suppression, contrast preservation and hepatic cyst detection in LDCT images are achieved by using the proposed method ( P Conclusion With the tube current reduced to approximate one-fifth of the standard tube current setting, clinically acceptable images can still be obtained by using the proposed method.