Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT‐framework

The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state‐of‐the‐art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015     

钢环加固圆钢管混凝土柱-RC梁角节点轴压性能有限元分析

针对圆钢管混凝土柱-RC梁角节点,提出了对角节点的节点区采用多段钢环加固的构造方案,并对加固节点模型的轴压性能开展有限元分析研究。通过已有研究成果验证了有限元模型分析方法的可靠性,继而采用ABAQUS有限元分析软件建立152个加固节点模型进行轴压试验模拟。有限元分析结果表明,以多段钢环加强的圆钢管钢筋混凝土柱-RC梁角节点轴压性能优于普通节点,加固节点具有更高的轴压承载力和更好的延性,加固效果明显。参数分析表明：加固角节点的轴压承载力随节点混凝土轴心抗压强度、节点区钢环体积占比和节点区纵筋配筋率的增加而增加;角节点的轴压承载力受梁端剪力影响,较大的梁端剪力可能造成节点延性降低。根据分析结果,建立了节点区采用钢环加固的圆钢管约束混凝土柱-RC梁角节点的轴压承载力算式,供设计该类节点参考。     

硫酸镁联合罗哌卡因股神经阻滞对膝关节韧带修复术患者下肢止血带反应的疗效观察

目的:探讨硫酸镁联合罗哌卡因股神经阻滞对膝关节韧带修复术下肢止血带反应的疗效。方法:选择2016年2月至2018年2月于本院拟行关节镜下膝关节韧带修复术的患者83例作为本研究对象,随机分为试验组（n=42）和对照组（n=41）;两组均在超声引导下实施患侧股神经阻滞麻醉,试验组给予0.375%罗哌卡因15 mL（含有0.75 g硫酸镁）混合液注射,对照组给予0.375%罗哌卡因15 mL注射,两组麻醉成功后均于大腿中上1/3处缚扎自动充气止血带。比较两组感觉和运动阻滞起效时间、持续时间和镇痛时间、不同时间点血流动力学和血清丙二醛（MDA）、肿瘤坏死因子-α（TNF-α）的变化,记录止血带反应发生率和药物不良反应。结果:与对照组相比,试验组感觉阻滞和运动阻滞起效时间缩短,持续时间、镇痛时间延长（P<0.05）;两组不同时间点收缩压（SBP）、舒张压（DBP）、心率（HR）比较,差异均具有统计学意义（P<0.05）;试验组T7、T8血清MDA、TNF-α高于对照组（P<0.05）;两组止血带反应发生率分别为7.14%（3/42）和24.39%（10/41）,差异具有统计学意义（P<0.05）;两组不良反应发生率差异无统计学意义（P>0.05）。结论:在膝关节韧带修复术中应用硫酸镁混合罗哌卡因股神经阻滞效果显著,可有效维持血流动力学稳定、延长阻滞效果,缓解氧化应激和炎症反应,减少止血带反应,安全性高,值得应用推广。     

Dendrimer as nanocarrier for drug delivery

Dendrimers are novel three dimensional, hyperbranched globular nanopolymeric architectures. Attractive features like nanoscopic size, narrow polydispersity index, excellent control over molecular structure, availability of multiple functional groups at the periphery and cavities in the interior distinguish them amongst the available polymers. Applications of dendrimers in a large variety of fields have been explored. Drug delivery scientists are especially enthusiastic about possible utility of dendrimers as drug delivery tool. Terminal functionalities provide a platform for conjugation of the drug and targeting moieties. In addition, these peripheral functional groups can be employed to tailor-make the properties of dendrimers, enhancing their versatility. The present review highlights the contribution of dendrimers in the field of nanotechnology with intent to aid the researchers in exploring dendrimers in the field of drug delivery.     

大型医疗设备电气设计要点

本文详细介绍了磁共振设备（MRI）、加速器、X射线设备等医院建筑中常用的大型医疗设备的工作原理、临床应用、负荷特性和主要电气参数，并对相应设备的供电方案、配电系统、照明和接地等主要电气设计要点加以说明，可为电气设计人员提供参考。     

A multidimensional segmentation evaluation for medical image data

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.     

IN-PLANE MOISTURE TRANSPORT IN PAPER DETECTED BY MAGNETIC RESONANCE IMAGING

Magnetic resonance imaging was used to visualize in-plane moisture transport in laboratory-made handsheets, heavy paperboard, and polyethylene-coated paperboard. Beginning with wet samples sealed on both surfaces, the moisture content was reduced through evaporation from the outside edges. The diffusion of moisture to the outside edges, i.e., in the plane of the sheets, was found to be isotropic with respect to the sample machine and cross directions. Isotropic in-plane moisture diffusion was observed for samples exhibiting a relatively high degree of fiber orientation, and under conditions of forced convection with air flow rates up to 10 L/min past the outside edges.     

Image based meshing of packed beds of cylinders at low aspect ratios using 3d MRI coupled with computational fluid dynamics

CFD is a valuable tool for understanding the flow and pressure drop in packed beds. However, determining the geometry can be complex. One possible method is to use a non-invasive imaging method such as MRI, however, problems occur in processing complex geometries when using traditional commercial meshing software. This work focuses on the use of image based meshing software originally developed for the field of computational biomechanics, to create geometries from 3d MRI scans of packed beds for use with computational dynamics. For this work we focus on disordered packed beds of cylinders at low aspect ratios and Reynolds numbers of Re = 1431-5074 (based on particle diameter and superficial velocity). We compare CFD studies with experimental data performed on the actual scanned beds and compare these with the correlation proposed by Eisfeld and Schnitzlein (2001). Computational data is shown to correlate well with experimental and theoretical results.     

Experiences on data quality in automatic tissue classification

Automatic intensity-based tissue classification sets requirements for the quality of multispectral magnetic resonance (MR) images. Tests for evaluating the separability of tissue classes, and on the other hand class distances required to obtain reliable classification, are presented in this study. Intraslice, interslice and interpatient training schemes for 5-nn classification were considered. Interslice training was utilized in classification of images from 10 patients with ischemic stroke giving results of satisfactory but highly variable quality. Based on the experience with these data sets, similar tests are recommended before imaging a large patient series in order to avoid extra manual work and to obtain reliable classification results.     

Mr and the pathogenesis of musculoskeletal disease

NMR imaging has become an important diagnostic tool in the evaluation of musculoskeletal disease. However, its ability to evaluate pathogenic mechanisms of disease may eventually have even a greater impact on patient care. NMR imaging has significantly affected our understanding of the clinical significance of meniscal tears and appropriate patient management. It has also extended our understanding of the prevalence and importance of X-ray and arthroscopically occult bone, tendon, and ligament injuries. By knowing the pathogenic mechanisms of disease, we can more reliably diagnose and treat pathology. In particular, many horizontal degenerative tears in older patients may be asymptomatic and are best left in place. Many acute tears may be best treated with immediate meniscal repair. Magnetic resonance (MR) imaging can reliably differentiate these tear types with noninvasive imaging. The ability of MR to detect bone injuries may lead to aggressive early non-weight-bearing therapy on injured bone, thereby preserving the subchondral bone from collapse and the joint from secondary osteoarthrosis. The ability of MRIs to detect and stage degenerative tendonosis of tendons and ligaments, such as the rotator cuff, may lead to arthroscopic decompression or debridement which provides symptomatic relief and may preserve and protect the intact cuff.