虚拟膝关节镜手术中保持单元质量的体网格形变方法

在虚拟膝关节手术中,需要对膝关节进行大范围形变的实时模拟。本文针对四面体网格的膝关节模型,提出了采用LSD度量建立形变能量,然后将带约束的最优化问题转化为不带约束的最优化问题,最后通过带Armijo线性查找的非精确牛顿法求解最优化问题。在求解过程中,通过预估未知点的位置,减少迭代步数,提高了算法的效率。这种方法具有较好的保体积性,同时保证形变后的四面体网格不出现体元翻转和退化。该方法也能推广应用于其它类似的关节弯曲运动的变形中。     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

基于非下采样Contourlets的CT／MRI图像自适应融合

结合人类视觉特性,针对CT／MRI医学图像的特点,提出了一种基于非下采样Contourlet变换的图像融合算法。先对源图像作非下采样Contourlet变换,完成图像的多尺度分析和方向分析。充分考虑各尺度分解层的系数特征,对低通子带,基于评价准则最优,采用免疫克隆选择优化策略迭代获取近似最优融合权值;对高通子带则选取绝对值最大作融合。实验结果表明：分别与基于小波、非下采样小波,以及Contourlet的融合结果相比较,文中融合算法获得的融合图像边缘的清晰度,以及整体的对比度都有所改善。     

一种基于遗传算法的脑MR图像去偏移场模型

由于磁共振图像(magnetic resonance images,MRI)常含有偏移场而影响后继图像分割,针对这种图像的分割,采用Legendre多项式基函数来拟合偏移场,可以去除偏移场对图像分割的影响。当使得恢复图像的信息熵达到最小时,则求得的偏移场最优。在求偏移场的过程中,需要求解基函数的参数,由于传统的梯度下降法易陷入局部最优,为解决此问题,提出将遗传算法引入到参数求解过程中,然而传统的遗传算法不仅时间复杂度高,且易陷入局部最优,为此需对遗传算法进行改进,使得不仅更容易得到全局最优解,且时间复杂度较低。实验证明,该改进算法可以得到精确的偏移场,并可得到准确的分割结果。     

Turbo spin-echo sequences in magnetic resonance imaging of the brain: Physics and applications

Fast SE imaging provides considerable measure time reduction, high signal-to-noise ratios as well as similar contrast behavior compared to conventional SE sequences. Besides TR and TE_eff, echo train length (ETL), interecho time , and-space trajectory determine image contrast and image quality in fast SE sequences. True proton density contrast (CSF hypointense) and not too strong T2 contrast are essential requirements in routine brain MRI. A Turbo SE sequence with very short echo train length (ETL=3), short TE_eff and short interecho time (17 ms), and TR=2000 ms was selected for proton density contrast; a Turbo SE sequence with ETL=7, TE_eff=90 ms, =22 ms, and TR=3250 ms was selected for T₂-weighted images. Using both single-echo Turbo SE sequences yielded 50% measure time reduction compared to the conventional SE technique. Conventional SE and optimized Turbo SE sequences were compared in 150 patients resulting in very similar signal and contrast behavior. Furthermore, reduced flow artifacts in proton density—and especially in T₂-weighted Turbo SE images—and better contrast of high-intensity lesions in proton density-weighted Turbo SE images were found. Slightly reduced edge sharpness—mainly in T₂-weighted Turbo SE images—did not reduce diagnostic reliability. Differences between conventional and Turbo SE images concerning image contrast and quality are explained regarding special features of fast SE technique.Address for correspondence: Institut für Röntgendiagnostik, Klinikum der Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93042 Regensburg, Germany. Additional reprints of this chapter may be obtained from the Reprints Department, Chapman & Hall, One Venn Plaza, New York, NY 10119.     

基于双解码路径DD-UNet的脑肿瘤图像分割算法

针对医学图像中病灶区域尺度不一、边界模糊和周围组织强度不均匀所导致的分割精度降低问题,提出了一种基于双解码器的脑肿瘤图像分割模型。为了增强特征的表征力,提出了高阶微分残差模块并使用不同空洞率的扩张卷积用于提取特征编码,提高了网络模型的分割性能;引入上下文语义信息感知模块(multi scale dilation, MSD),从不同的目标尺度中提取更多的精细信息,提高了对结构细节信息的捕获能力,同时减少了编解码器之间的特征差异;在空间解码路径中使用选择性聚合空间注意力模块(spatial aggregation attention module, SAAM),增加了对有效空间特征的权重比例,减少了无效的特征干扰。在脑肿瘤数据集上进行了实验验证,实验结果表明,所提算法的Dice系数、平均交并比、敏感性、特异性、准确率等指标分别为：93.35%、90.71%、91.15%、99.94%、96.75%。     

Catalase-imprinted Fe<Subscript>3</Subscript>O<Subscript>4</Subscript>/Fe@fibrous SiO<Subscript>2</Subscript>/polydopamine nanoparticles: An integrated nanoplatform of magnetic targeting,magnetic resonance imaging,and dual-mode cancer therapy

Recent advances in the research on the molecular mechanism of cell death and methods for preparation of nanomaterials make the integration of various therapeutic approaches,targeting,and imaging modes into a single nanoscale complex a new trend for the development of future nanotherapeutics.Hence,a novel ellipsoidal composite nanoplatform composed of a magnetic Fe3O4/Fe nanorod core (～120 nm) enwrapped by a catalase (CAT)-imprinted fibrous SiO2/ polydopamine (F-SiO2/PDA) shell with thickness 70 nm was prepared in this work.In vitro experiments showed that the Fe3O4/Fe@F-SiO2/PDA nanoparticles can selectively inhibit the bioactivity of CAT in tumor cells by the molecular imprinting technique.As a result,the H2O2 level in tumor cells was elevated dramatically.At the same time,the Fe3O4/Fe core released Fe ions to catalyze the conversion of H2O2 to ·OH in tumor cells.Eventually,the concentration of ·OH in tumor cells rapidly rose to a lethal level thus triggering apoptosis.Combined with the remarkable near-infrared light (NIR) photothermal effect of the CATimprinted PDA layer,the Fe3O4/Fe@F-SiO2/PDA nanoparticles can effectively kill MCF-7,HeLa,and 293T tumor cells but are not toxic to nontumor cells.Furthermore,these nanoparticles show good capacity for magnetic targeting and suitability for magnetic resonance imaging (MRI).Therefore,the integrated multifunctional nanoplatform opens up new possibilities for high-efficiency visual targeted nonchemo therapy for cancer.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

Online dynamic cardiac imaging based on the elastic-net model

Purpose: The purpose of this work was to develop an online dynamic cardiac MRI model to reconstruct image frames from partial acquisition of the Cartesian k-space data, which utilizes structural knowledge of consecutive image frames. Materials and methods: Using an elastic-net model, the proposed algorithm reconstructs dynamic images using both L₁ and L₂ norm operations. The L₁ norm enforces the sparsity of the frame difference, while the L₂ norm with motion-adaptive weights catches the internal structure of frame differences. Unlike other online methods such as the Kalman filter (KF) technique, the new model requires no assumption of Gaussian noise, and can faithfully reconstruct the dynamic images within a compressive sensing framework. Results: The proposed method was evaluated using simulated dynamic phantoms with 40 frames of images (128?×?128) and a cardiac MRI cine of 25 frames (256?×?256). Both results showed that the new model offered a better performance than the online KF method in depicting simulated phantom and cardiac dynamics. Conclusion: It is concluded that the proposed imaging model can be used to capture a large variety of objects in motion from highly under-sampled k-space data, and being particularly useful for improving temporal resolution of cardiac MRI.