Objective To explore the topological properties of the brain structural network in patients with neuromyelitis optica spectrum disorder (NMOSD).Methods Diffusion tensor imaging was performed in 41 NMOSD patients (patient group) and 40 age-and sex-matched healthy volunteers (control group) who were admitted to the Department of Neurology,The Third Affiliated Hospital to Sun Yat-sen University from September 2014 to October 2017.The deterministic fiber tracking techniques were used to construct the white matter structural weighted network.Topological properties of the brain structural network were then calculated based on complex graph theory analysis.The 2 groups were compared in terms of global and local parameters of the brain structural network using statistical methods.Results The brain structural networks in both groups exhibited small world properties.Compared with the control group,the global efficiency of the brain structural network in the patient group was significantly decreased and the shortest path length significantly increased (P=0.002,P=0.002,FDR correction).There were no statistically significant differences between the brain structural networks of the 2 groups in terms of clustering coefficient,the shortest path length on average,value of small world property,average clustering coefficient or local efficiency (P=0.780,P=0.496,P=0.279,P=0.269,P=0.050,FDR correction).Compared with the control group,the nodal efficiency of the brain structural network of the patient group was significantly decreased in the frontal lobe (bilateral precentral gyrus,middle frontal gyrus of the right orbital part,inferior frontal gyrus of the right opercular part,right rolandic operculum,bilateral median cingulate and paracingulate gyri),parietal lobe (right posterior cingulate gyrus,right superior parietal gyrus,left inferior parietal of angular gyri,right angle gyrus,and right precuneus),temporal lobe (bilateral hippocampus and right parahippocampal gyrus),occipital lobe (left cuneus,left superior occipital gyms,bilateral middle occipital gyrus,and left inferior occipital gyrus) and subcortical region (right caudate nucleus and right thalamus) (P＜0.05,FDR correction).Conclusion There is abnormal connection in brain structural network in NMOSD patients.

We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis.
Algorithms ; Artifacts ; Humans ; Image Processing, Computer-Assisted ; methods ; Kidney Glomerulus ; ultrastructure ; Microscopy, Electron, Transmission ; methods

We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis.

We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis.

Radiographic detection of pulmonary nodules based on three-dimensional Hessian matrix is highly sensitive but frequently produces false positive results in areas where blood vessels intersect. We propose a novel approach to pulmonary nodule detection using Hessian matrix-based adaptive window structure analysis, in which the structure coefficients is used to differentiate a voxel that belongs to a nodule or vascular structures, followed by construction of the 3D adaptive window to analyze the local structure characteristics; the nodules were then detected using the discrimination function. The experimental results on pulmonary CT images from 17 patients showed a 100% detection sensitivity for nodules of varying sizes and types, with also significantly reduced false positive results generated by the vessel junctions. This approach provides valuable assistance to follow-up positioning and segmentation of the pulmonary nodules.
Humans ; Lung ; pathology ; Lung Neoplasms ; diagnosis ; Tomography, X-Ray Computed

Radiographic detection of pulmonary nodules based on three-dimensional Hessian matrix is highly sensitive but frequently produces false positive results in areas where blood vessels intersect. We propose a novel approach to pulmonary nodule detection using Hessian matrix-based adaptive window structure analysis, in which the structure coefficients is used to differentiate a voxel that belongs to a nodule or vascular structures, followed by construction of the 3D adaptive window to analyze the local structure characteristics;the nodules were then detected using the discrimination function. The experimental results on pulmonary CT images from 17 patients showed a 100%detection sensitivity for nodules of varying sizes and types, with also significantly reduced false positive results generated by the vessel junctions. This approach provides valuable assistance to follow-up positioning and segmentation of the pulmonary nodules.

Radiographic detection of pulmonary nodules based on three-dimensional Hessian matrix is highly sensitive but frequently produces false positive results in areas where blood vessels intersect. We propose a novel approach to pulmonary nodule detection using Hessian matrix-based adaptive window structure analysis, in which the structure coefficients is used to differentiate a voxel that belongs to a nodule or vascular structures, followed by construction of the 3D adaptive window to analyze the local structure characteristics;the nodules were then detected using the discrimination function. The experimental results on pulmonary CT images from 17 patients showed a 100%detection sensitivity for nodules of varying sizes and types, with also significantly reduced false positive results generated by the vessel junctions. This approach provides valuable assistance to follow-up positioning and segmentation of the pulmonary nodules.

OBJECTIVETo simulate the multi-leaf collimator of Varian linear accelerator using Monte Carlo method.

METHODSThe multi-leaf collimator model was established using the DYNVMLC module of BEAMnrc and validated by comparison of Monte Carlo simulation and actual measurement results.

RESULTSThe simulation results were well consistent with the actual measurement results with a bias of less than 3%.

CONCLUSIONThe multi-leaf collimator of Varian linear accelerator can be successfully modeled using Monte Carlo method for analysis of the impact of the geometric properties of the multi-leaf collimator on the dose distribution.

Objective To simulate the multi-leaf collimator of Varian linear accelerator using Monte Carlo method. Methods The multi-leaf collimator model was established using the DYNVMLC module of BEAMnrc and validated by comparison of Monte Carlo simulation and actual measurement results. Results The simulation results were well consistent with the actual measurement results with a bias of less than 3%. Conclusion The multi-leaf collimator of Varian linear accelerator can be successfully modeled using Monte Carlo method for analysis of the impact of the geometric properties of the multi-leaf collimator on the dose distribution.

Objective To simulate the multi-leaf collimator of Varian linear accelerator using Monte Carlo method. Methods The multi-leaf collimator model was established using the DYNVMLC module of BEAMnrc and validated by comparison of Monte Carlo simulation and actual measurement results. Results The simulation results were well consistent with the actual measurement results with a bias of less than 3%. Conclusion The multi-leaf collimator of Varian linear accelerator can be successfully modeled using Monte Carlo method for analysis of the impact of the geometric properties of the multi-leaf collimator on the dose distribution.