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To assess whether the joint application of hybrid iterative reconstruction (HIR) and an adaptive filter (AF) could reduce streak artifacts and improve image quality of neck-and-shoulder computed tomography (CT).This study included 96 patients with suspicious neck lesions who underwent a routine nonenhanced scan on a 64-slice CT scanner. The raw data were reconstructed using four different settings: filtered back projection (FBP), HIR, FBP + AF, and HIR + AF. Regions of interest were manually drawn in erector spine, axillary fat, latissimus dorsi, and dorsal cervical fat. Mean and standard deviation (SD) of the CT number, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained and compared using Wilcoxon signed-rank tests. The qualitative assessments of five factors were compared by two independent investigators.Compared to the other three settings, HIR + AF reduced noise in the area where the streak artifact of the lower neck were most serious (SD; all p ≤ 0.001). The SNR and CNR were improved significantly (all p ≤ 0.001). Compared to the other three settings, HIR + AF showed a significant improvement in CT image quality regarding the visibility of suspicious lesions, the extent of streaking artifacts, noise, soft-tissue contrast, and visualization of small structures (all p ≤ 0.02).The combination of HIR and AF can significantly reduce streaking artifacts and improve image quality in neck-and-shoulder CT imaging.

To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules.CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep-learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann-Whitney U test and t-test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy.Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p 0.05). Forty-four features were significantly different between benign and malignant nodules (p 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74-0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68-0.86) with the test set.With the CT texture analysis model trained with deep-learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

Parastagonospora nodorum is an important pathogen of wheat. The contribution of secondary metabolites to this pathosystem is poorly understood. A biosynthetic gene cluster (SNOG_08608-08616) has been shown to be upregulated during the late stage of P. nodorum wheat leaf infection. The gene cluster shares several homologues with the Cercospora nicotianae CTB gene cluster encoding the biosynthesis of cercosporin. Activation of the gene cluster by overexpression (OE) of the transcription factor gene (SNOG_08609) in P. nodorum resulted in the production of elsinochrome C, a perelyenequinone phytotoxin structurally similar to cercosporin. Heterologous expression of the polyketide synthase gene elcA from the gene cluster in Aspergillus nidulans resulted in the production of the polyketide precursor nortoralactone common to the cercosporin pathway. Elsinochrome C could be detected on wheat leaves infected with P. nodorum, but not in the elcA disruption mutant. The compound was shown to exhibit necrotic activity on wheat leaves in a light-dependent manner. Wheat seedling infection assays showed that ΔelcA exhibited reduced virulence compared with wild type, while infection by an OE strain overproducing elsinochrome C resulted in larger lesions on leaves. These data provided evidence that elsinochrome C contributes to the virulence of P. nodorum against wheat.

Background The term bone quality is often used in a dentomaxillofacial context, for example in implant planning, as bone density and bone structure have been linked to primary implant success. Purpose This research aimed to investigate the performance of adaptive thresholding of trabecular bone in cone beam CT (CBCT) images. The segmentation quality was assessed for different imaging devices and upper and lower jaws. Materials and Methods Four jaws were scanned with eight CBCT scanners and one micro-CT device. Images of the jaws were spatially aligned with the micro-CT images. Two volumes of interest for each jaw were manually delineated. Trabecular bone in the volumes of interest in the micro-CT images was segmented so that the micro-CT images could serve as high-resolution ground truth images. The volumes of interest in the CBCT images were segmented using both global and adaptive thresholding. Results Segmentation was significantly better for the lower jaw than for the upper jaw. Differences in performance between the scanners were significant for both jaws. Adaptive thresholding performed significantly better in segmenting the bone structure out of CBCT images. Conclusions When assessing jaw bone structure, the observer should always choose adaptive thresholding. It remains a challenge to identify the optimal threshold selection for the structural assessment of jaw bone.

Magnesium is light, biocompatible and has similar mechanical properties to natural bone, so it has the potential to be used as a biodegradable material for orthopedic applications. However, pure magnesium severely corrodes in a physiological environment, which may result in fracture prior to substantial tissue healing. Hydroxyapatite (HA) is the main composition of natural bone. It has excellent bioactivity and osteoconductivity. In this study, HA coating with two different thicknesses was applied onto the surface of pure magnesium substrates using a biomimetic technique. The corrosion rate of the surface-treated substrates was tested. It was found that both types of coatings substantially slowed down the corrosion of the substrate, and the dual coating was more effective than the single coating in hindering the degradation of the substrate. Thus, the corrosion rate of magnesium implants can be closely tailored by adjusting apatite coating thickness and thereby monitoring the release of magnesium ions into the body.

An anatomically realistic rat model was developed from color images of successive cryosections of a mature Sprague–Dawley rat. Images were obtained, by digital scanning, of 9475 slices with thickness of 0.02 mm. A total of 13 major organs and tissues were selected, and models of these organs and tissues constructed from the images were used for calculations of absorbed dose from external photon sources. A detailed set of conversion coefficients from kerma free-in-air to organ absorbed dose have been calculated for external monoenergetic photon beams with energies ranging from 10 keV to 10 MeV under five idealized irradiation conditions (left lateral, right lateral, dorsal-ventral, ventral-dorsal, and isotropic) using the Monte Carlo code MCNPX. Dose results are presented in form of tables as supplemental data for practical use and comparison. The influence of anatomical characteristics, including organ volume, shape, location, and orientation, on dose distributions were evaluated. It would also be possible to make internal dose assessments using the computational rat model.