Low-field magnetic resonance imaging of the pelvis in patients with anal dynamic graciloplasty: initial experience

The aim of this study was to determine whether low-field magnetic resonance (MR) imaging can safely and accurately depict inflammatory changes in patients with anal dynamic graciloplasty, in whom high-field MR imaging is contraindicated and ultrasonography and computed tomography are inadequate. A 0.2-T field-strength MR examination was performed in six patients with anal dynamic graciloplasty malfunction in whom reoperation was contemplated. The following sequences were applied:T ₂-weighted turbo spinecho with fat saturation,T ₁-weighted conventional spin-echo, and contrastenhancedT ₁-weighted conventional spin-echo with fat saturation. Results indicated that none of the patients experienced relevant discomfort, pacemaker malfunction, or electrode dislocation with low-field MR imaging. Inflammatory pelvic changes were visualized in four patients and atrophy of the transposed gracilis muscle in another. Surgery was thus avoided in the four, who underwent conservative treatment for their pelvic inflammation. It was concluded that these prelininary results demonstrate the feasibility of MR imaging with a low field strength in patients with anal dynamic graciloplasty. In such patients, in whom diagnostic imaging had been problematic, the potential for safe and accurate visualization will be a boon to treatment planning.     

Retrospective analysis for evaluation of the value of contrast-enhanced MRI in patients treated with breast conservative therapy

Purpose The aim of this study was to assess the value of contrast-enhanced (c.-e.) MRI in the follow-up of patients with conservatively treated breast cancer since detection and exclusion of malignancy may interfere significantly with posttherapeutic changes within the treated breast. Material and methods A total of 207 patients with a history of limited surgery and radiation therapy underwent MR imaging, 40 patients were examined 0–12 months and 167 patients were examined later than 12 months after radiotherapy. Suspicious or indeterminate findings were suggested by clinical examination or conventional imaging in 80 studies. In 127 women, MRI was performed within breast tissue that was difficult to assess due to scarring or dense breast tissue. Results Recurrent carcinoma was confirmed in 27 patients by surgical biopsy. All 27 carcinomas, except for one with a slow signal increase, demonstrated early rise of signal intensity on dynamic T1-weighted contrast enhanced images. During the first year after therapy, the diagnostic accuracy could not be improved by additional use of c.-e. MRI. Differentiation between posttherapeutic changes and recurrent carcinoma was frequently not possible because of strong and sometimes early and ill-circumscribed enhancement. Later than 12 months after therapy enhancement decreased significantly, thus the false positive calls could be reduced from 49 (conventional imaging) to 12 (conventional imaging plus MRI). A total of 12 of 26 recurrences and multifocality in 4/5 cases were diagnosed by MR imaging alone at this time interval. Conclusion In the first year after therapy, c.-e. MRI is only indicated in selected cases. The results later than 12 months emphasize that c.-e. MRI may contribute significant additional information. It allows better distinction of posttherapeutic fibrosis from recurrent carcinoma and proved to be able to detect recurrent disease more sensitive and at an earlier stage.     

基于MS-FCM算法的MR图像分割方法

针对传统模糊C-均值(FCM)聚类算法在分割低信噪比图像时准确性较差的问题,提出一种用于MR图像分割的改进算法MS-FCM。针对脑部MR图像相邻像素属于同一分类的模糊隶属度相近的特性,在迭代过程中对隶属度数据集进行滤波,以降低噪声对聚类精度的影响。模拟脑部MR图像和临床脑部MR图像的分割实验证明,该算法可以提高图像分割精度。     

HPMR系统KV路由算法设计

提出一种针对HPMR系统的KV路由算法。HPMR系统是MapReduce模型的一个实现,改进了MapReduce模型以适应高性能计算的需求。HPMR的KV路由算法生成KV路由表,所有数据的收发动作将依据该路由表进行。KV路由算法产生的KV路由表直接决定HPMR系统在通信阶段的时间开销。实验结果表明,该KV路由算法产生的KV路由表可以提高HPMR系统的通信性能。     

MR编码在传真图像编码中的仿真应用

传真图像的信息量非常大,对其进行数据压缩,不仅可以节省存储空间,而且可以有效地降低传输时间。采用MR编码对传真图像进行数据压缩,设计了压缩和解压缩编码算法。仿真实验表明,该算法可以满足传真图像对压缩比和压缩速度的要求,且有效可行。     

Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

野草入侵模糊算法的半主动空气悬架研究

为了抑制由路面不平引起的车辆振动,结合磁流变阻尼器和空气弹簧的变阻尼/刚度特性,设计了含内置永磁体式磁流变阻尼器的半主动空气悬架系统.基于电磁学原理,对内置永磁体式磁流变阻尼器的力学特性建模.建立了1/4车辆二自由度动力学模型,并利用野草入侵算法对常规模糊算法规则进行优化,开发了野草入侵-模糊混合控制策略对内置永磁体磁流变阻尼器的空气悬架进行半主动控制.为验证该控制策略在磁流变空气悬架的半主动控制效果,进行了C级路面随机输入及凸块脉冲输入仿真分析,仿真结果可知,野草入侵-模糊控制策略能有效地提高半主动悬架系统的综合性能.并且通过台架试验进一步表明,利用该控制策略能够使车身振动加速度及悬架动挠度分别减小25.87%、35.13%.     

高寒地区高铁运用安全冗余及其转向架优配解决方案

为解决高寒地区高铁运用安全冗余问题,应用基于抗蛇行频带吸能机制的稳定新理论提出转向架优配方案．针对三大扰动或摄动影响,即蛇行振荡参振质量摄动、抗蛇行减振器性能摄动以及风荷侧向扰动,该转向架优配方案合理挖掘欧系车辆技术优势,因此高速列车具有十分理想的稳定鲁棒性能,经济时速可以达到350～380km／h．考虑到SACHS抗蛇行减振器的技术可靠性,高寒车最佳商业速度不得不降至300km／h．尽管如此,高寒车的运用必须是个积极稳健的推进过程,即由200km／h逐步提速,最终达到300km／h运用．     

A framework with modified fast FCM for brain MR images segmentation

Intensity inhomogeneity, noise and partial volume (PV) effect render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the effects listed above. In this paper, a framework with modified fast fuzzy c-means for brain MR images segmentation is proposed to take all these effects into account simultaneously and improve the accuracy of image segmentations. Firstly, we propose a new automated method to determine the initial values of the centroids. Secondly, an adaptive method to incorporate the local spatial continuity is proposed to overcome the noise effectively and prevent the edge from blurring. The intensity inhomogeneity is estimated by a linear combination of a set of basis functions. Meanwhile, a regularization term is added to reduce the iteration steps and accelerate the algorithm. The weights of the regularization terms are all automatically computed to avoid the manually tuned parameter. Synthetic and real MR images are used to test the proposed framework. Improved performance of the proposed algorithm is observed where the intensity inhomogeneity, noise and PV effect are commonly encountered. The experimental results show that the proposed method has stronger anti-noise property and higher segmentation precision than other reported FCM-based techniques.