基于B超图像评价HIFU治疗剂量对组织损伤程度的影响

基于B超图像分析HIFU治疗中辐照剂量、组织凝固性坏死区域和图像参数之间的关系。通过对高强度聚焦超声辐照新鲜离体组织前后获得的B超图像做数字减影处理,计算图像灰度平均值,同时切片观察并记录生物组织的凝固性坏死区域大小,在此基础上得到大批量数据的统计特性。结果表明：辐照剂量、组织凝固性坏死区域与B超图像灰度平均值在一定范围内成正相关性;当凝固性坏死区域增大到一定程度时,B超图像灰度平均值不再增大,而是呈无规律分布。B超图像灰度可反映组织损伤程度,为实时监控HIFU治疗效果提供依据。     

高强度聚焦超声治疗中测温技术的研究

高强度聚焦超声(HIFU)是一种新兴的非侵入性局部治疗技术,它以其治疗中安全、有效、无创的优点被医学界所关注.然而,对生物组织温度的准确测量是制约该治疗技术发展的关健环节,因此,HIFU治疗中的测温技术成为人们关注的热点.本文主要介绍了近年来在HIFU治疗中,测温技术的研究现状以及两大测温方法(无损测温和有损测温),并对测温技术的发展进行展望.     

基于灰度共生矩阵的人体皮肤纹理分析

对纹理图像的分析及特征提取是近年来图像处理领域的研究热点,在现实中有广泛的应用价值。灰度共生矩阵已被理论证明是图像纹理分析的一个很好的方法。为了使灰度共生矩阵所提取的特征值能够更好地描述皮肤老化的灰度分布信息,以不同年龄层的女性的不同部位的皮肤纹理图像作为研究对象,对采集到的图像进行预处理,采用灰度共生矩阵法提取纹理的特征值,通过统计分析得出了特征值的变化规律。实验结果对皮肤老化研究及其纹理分析有参考意义。     

基于神经网络中的NNTool对小鼠皮肤的识别方法

研究皮肤纹理经过UVB光照射后的变化情况并对其进行识别。具体地,采取图像纹理分析方法,对经过光照射后不同时期的小鼠皮肤图像提取纹理特征,进而建立一种新的皮肤纹理识别模型。采用空间灰度共生矩阵法提取图像纹理的4个主要特征,即:能量,熵,惯性矩,相关度,然后利用神经网络中的NNtool对皮肤纹理图像进行训练和分类识别。实验结果很好地证明了这种纹理分析和识别方法的可行性和有效性。     

使用图像特征构建快速有效的蛋白质折叠识别方法

蛋白质结构自动分类是探索蛋白质结构- 功能关系的一种重要研究手段。首先将蛋白质折叠子三维空间结构映射成为二维距离矩阵,并将距离矩阵视作灰度图像。然后基于灰度直方图和灰度共生矩阵提出了一种计算简单的折叠子结构特征提取方法,得到了低维且能够反映折叠结构特点的特征,并进一步阐明了直方图中零灰度孤峰形成原因,深入分析了共生矩阵特征中灰度分布、不同角度和像素距离对应的结构意义。最后应用于27类折叠子分类,对独立集测试的精度达到了71.95 %,对所有数据进行10 交叉验证的精度为78.94 %。与多个基于序列和结构的折叠识别方法的对比结果表明,此方法不仅具有低维和简洁的特征,而且无需复杂的分类系统,能够有效和高效地实现多类折叠子识别。     

超声造影剂协同下的高强度聚焦超声对包虫原头蚴酶活性的影响

为探索高强度聚焦超声结合适宜比例的微泡造影剂对离体细粒棘球绦虫原头蚴酶活性的影响,实验分离包虫原头蚴,在原头蚴悬液中加入比例为1:80的微泡造影剂(含氟脂质体微泡),以声功率为50W的高强度聚焦超声波辐照30s,辐照后悬液涂片作酶组织化学染色,检测原头蚴三磷酸腺苷酶、葡萄糖-6-磷酸酶和琥珀酸脱氢酶的活性。结果显示,超声剂量相同时,微泡造影剂组的原头蚴三磷酸腺苷酶和琥珀酸脱氢酶活性明显低于单纯超声辐照组(P<0.05);葡萄糖-6-磷酸酶活性有一定降低;阴性对照组无酶反应物产生。高强度聚焦超声结合微泡造影剂能增强对离体原头蚴三磷酸腺苷酶和琥珀酸脱氢酶活性的抑制作用。     

高强度聚焦超声肿瘤治疗焦域的仿真研究

数值仿真是预测高强度聚焦超声(high intensity focused ultrasound,HIFU)治疗的温度分布、确定治疗剂量的有效方法之一。本研究采用Westervelt方程的近似式,并结合Pennes生物热传导方程,以猪肝肿瘤为例,在考虑肝组织声学特性对HIFU温度场影响的条件下,通过时域有限差分法仿真研究辐照时间和声强对肿瘤组织内可治疗焦域体积的影响。研究结果表明,一定声强条件下,肝组织声学特性对肿瘤内可治疗焦域的影响随着辐照时间的延长而凸显;可治疗焦域体积随时间增长或声强增大而非线性增加;相同辐照条件下,肿瘤组织内的可治疗焦域体积大于肝组织内的;当可治疗焦域体积一定时,辐照声强和辐照时间呈负相关;同时,等效热剂量判定的可治疗焦域大于温度阈值判定的可治疗焦域,且二者之差随声强而变化。     

人体皮肤的双光子图像纹理特征提取方法

近年来通过计算机技术对瘢痕实现无损诊断的研究进展迅速,其对瘢痕图像纹理特征的量化分析得到了很好的诊断效果。在这个过程中出现了很多纹理描述方法,这些方法的提出也促进了纹理研究的发展。本文在对灰度共生矩阵(GLCM)、局部三值模式(LTP)等统计纹理分析方法进行介绍的情况下,在瘢痕图像上利用梯度迭代回归树算法给出了不同方法的实验结果,得到了不同方法的回归模型。这些模型的性能体现在对不同年龄瘢痕的预测能力,其中局部差异局部二值模式(LD-LBP)和局部方向三值模式(LOTP)得到的模型预测能力最好,说明它们是目前对瘢痕图像纹理描述比较准确的方法之一,同时表明统计纹理分析方法适合用于瘢痕图像的纹理研究。     

高强度聚焦超声在药物控制释放的应用及进展

高强度聚焦超声能够以一种非侵入性的方式有效地穿透身体内部组织,聚焦在深层组织中一个很小的空间区域内,产生很强的声能,这些能量被组织吸收引起局部温度的升高。当温度到达热敏脂质体的相变温度时,磷脂烷基链构象的会发生改变,导致脂质体的通透性增强,从而能够促进药物的释放。因此,高强度聚焦超声可以被用作外源刺激控制体内特定位置热敏脂质体的药物释放。本文对高强度聚焦超声在药物控制释放领域的应用及进展进行综述。     

基于计算机视觉的棉铃虫成虫雌雄自动判别研究

棉铃虫Helicoverpa armigera(Hübner)是棉花作物的主要寄主害虫,其性别的自动准确判别对区域性比、种群数量等方面的预测预报具有重要意义。本文通过CCD设备获取雌雄成虫的原始彩色图像,运用数学形态学和自适应图像增强法进行滤波分析,提高害虫RGB彩色图像及分通道图像的质量。针对RGB彩色图像提取害虫的颜色矩特征,针对B通道灰度图像提取基于灰度共生和差分统计矩阵的纹理、形态不变矩等特征,对提取的36个特征数据进行归一化处理。将惩罚因子和RBF核函数参数作为SVM分类器识别率的重要判断标准,利用K折交叉验证选取最优参数组合并建立模型,当C=4,g=0.0825,识别率达到最佳98.33%。证实了将计算机视觉应用于昆虫性别自动判别的可行性。     

High-Intensity Focused Ultrasound in Functional Neurosurgery

Stereotactic functional neurosurgical interventions for basal ganglia are an important method for treating pain, obsessive–compulsive, movement and depressive disorders. These interventions include destructive surgeries and deep brain stimulation through implanted electrodes. Destructive surgeries have a number of serious limitations, since they are associated with a higher risk of complications, especially in case of bilateral interventions. High-intensity focused ultrasound (HIFU) is a novel noninvasive approach proposed for destruction of a certain target point in the brain. We discuss the technical foundations of HIFU, thoroughly analyze the advantages and drawbacks of the method compared to other methods of modern functional neurosurgery, and summarize the first results of using HIFU in the world’s leading clinics. Further accumulation of experience is needed to perform a well-considered analysis of the potential of HIFU and to assess the long-term effects of the interventions performed and the role of this procedure in the algorithms for treating various nervous system diseases.     

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.     

Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time‐Varying Covariates

Summary Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA‐GMM) approach to estimate marginal regression models for imaging datasets that contain time‐varying, spatially related responses and some time‐varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject’s response image at each time point) as many current neuroimaging analysis techniques do, this method “adaptively smoothes” neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA‐GMM’s development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer’s Disease Neuroimaging Initiative are used to assess the performance of MA‐GMM. Martha Skup, Hongtu Zhu, and Heping Zhang for the Alzheimer’s Disease Neuroimaging Initiative.     

Preliminary Investigation of a 64-element Capacitive Micromachined Ultrasound Transducer (CMUT) Annular Array Designed for High Intensity Focused Ultrasound (HIFU)

Background

Treatment of prostate cancer using endocavitary High Intensity Focused Ultrasound (HIFU) has become more commonplace since the first treatments in the 1990s. The gold standard HIFU strategy to treat prostate cancer is the complete thermal ablation of the entire prostate gland under real-time ultrasound (US) image guidance. A more desirable treatment and the current trend, however, is towards a focal treatment but more accurate and finely tunable thermal lesions are needed along with improved US imaging guidance. In this study, Capacitive Micromachined Ultrasound Transducer (CMUT) technology is being investigated, as they have shown recent promise for US imaging and potential to be used for HIFU therapy. They offer potential advantages over current piezoelectric designs in the context of ultrasound-guided HIFU (USgHIFU) focal therapies.

Robust dose-response curve estimation applied to high content screening data analysis

Background and method

Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey’s biweight function.

Results and conclusion

Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;’s nlinfit nlinfit function and GraphPad’s Prism software.

     

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

ObjectivesThe accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.Materials and MethodsTo address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.ResultsWe compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).ConclusionExperimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.     

Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.     

Concurrent Visualization of Acoustic Radiation Force Displacement and Shear Wave Propagation with 7T MRI

Manual palpation is a common and very informative diagnostic tool based on estimation of changes in the stiffness of tissues that result from pathology. In the case of a small lesion or a lesion that is located deep within the body, it is difficult for changes in mechanical properties of tissue to be detected or evaluated via palpation. Furthermore, palpation is non-quantitative and cannot be used to localize the lesion. Magnetic Resonance-guided Focused Ultrasound (MRgFUS) can also be used to evaluate the properties of biological tissues non-invasively. In this study, an MRgFUS system combines high field (7T) MR and 3 MHz focused ultrasound to provide high resolution MR imaging and a small ultrasonic interrogation region (~0.5 x 0.5 x 2 mm), as compared with current clinical systems. MR-Acoustic Radiation Force Imaging (MR-ARFI) provides a reliable and efficient method for beam localization by detecting micron-scale displacements induced by ultrasound mechanical forces. The first aim of this study is to develop a sequence that can concurrently quantify acoustic radiation force displacements and image the resulting transient shear wave. Our motivation in combining these two measurements is to develop a technique that can rapidly provide both ARFI and shear wave velocity estimation data, making it suitable for use in interventional radiology. Secondly, we validate this sequence in vivo by estimating the displacement before and after high intensity focused ultrasound (HIFU) ablation, and we validate the shear wave velocity in vitro using tissue-mimicking gelatin and tofu phantoms. Such rapid acquisitions are especially useful in interventional radiology applications where minimizing scan time is highly desirable.