非线性梯度场下并行成像技术进展

随着高场MR设备的普及,由于传统梯度场的发展受到人体外周神经刺激(PNS)阈值的限制,使对快速成像技术的需求十分迫切,非线性梯度场并行成像技术由此得到越来越多的关注.本文介绍梯度场构建、并行接收、采样轨迹以及图像重建算法等技术,对非线性梯度场下并行成像技术的研究现状进行综述.     

扩散张量成像的算法实践与应用

与传统扩散加权成像(diffusion weighted imaging,DWI)相比,扩散张量成像(diffusion tensor imaging,DTI)能够更加准确的反映分子扩散的方向性,因而在显示脑白质纤维走行及白质相关的脑部疾病等方面显示出巨大的优越性.本文从医学的角度对该方法的算法实践与应用做一综述.     

弹性成像结合卡方自动交互检测法在鉴别乳腺肿块良恶性中的应用

目的利用卡方自动交互检测法开发一种简单的算法来鉴别乳腺肿块的良恶性。方法经过病理证实的201例乳腺肿块患者纳入此项研究,每例患者都有一个乳腺肿块经常规超声检查、声触诊组织成像(VTI)及声触诊组织量化(VTQ)技术成像,并测量出VTI面积比(肿块VTI面积或肿块二维面积)及肿块VTQ或腺体VTQ。利用受试者操作特性曲线评价各项超声弹性成像参数的诊断性能,再使用卡方自动交互检测法进行分类分析。结果分类算法包括肿块VTQ及VTI面积比,其深度为两个分支(肿块VTQ>3.958或≤3.958,如果肿块VTQ≤3.958接着考虑VTI面积比≤1.304或1.304~1.493或>1.493)。分类算法的AUC为0.901、灵敏度为98.2%、特异度为68.1%,应用该算法,有30.8%的病例可以避免活检。结论联合应用肿块VTQ及VTI面积比的分类算法具有较高的诊断性能,准确度达97%,减少30%以上不必要的穿刺活检。     

前瞻性心电门控及迭代重建算法在256层螺旋CT冠状动脉成像中的应用价值

目的:研究前瞻性门控及迭代算法在降低CT冠状动脉成像辐射剂量中的价值。方法:对回顾性心电门控(后门控),前瞻性心电门控(前门控),前瞻性心电门控迭代重建算法(前门控+迭代重建)冠状动脉CTA检查各60例患者的CT影像进行分析,评价其图像质量与辐射量。结果:图像质量评分分别为(4.40±0.69)、(4.20±0.69)、(4.20±0.77)三组之间对比无差(P>0.05),图像质量均能满足诊断需求。其有效辐射量(ED)分别为(19.95±1.48)mSv、(6.27±0.33)mSv与(4.51±0.44)mSv三组对比具有统计学意义(P<0.05)。结论:在一定心率范围中,运用前门控结合迭代重建技术和单纯使用前门控,或后门控技术所得冠状动脉CT成像图像质量无明显差异,但是辐射剂量明显的降低。     

基于一种改进的相位校正算法消除MRI运动伪影

背景：近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛。但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影。目的：去除MRI图像成像时产生的伪影,改善图像质量。方法：使用改进的相位矫正算法,并结合水平集算法去除图像伪影。去除伪影后使用模糊增强改善处理后图像的质量。结果与结论：实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想。     

基于一种改进的相位校正算法消除MRI运动伪影

背景:近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛.但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影.目的:去除MRI图像成像时产生的伪影,改善图像质量.方法:使用改进的相位矫正算法,并结合水平集算法去除图像伪影.去除伪影后使用模糊增强改善处理后图像的质量.结果与结论:实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想.     

基于梯度电感约束和GPU加速算法的磁共振双平面自屏蔽梯度线圈设计方法

目的 针对磁共振成像系统(Magnetic Resonance Imaging,简记为MRI)梯度线圈的自感和互感引起的涡流,会抑制梯度磁场的快速变化,影响梯度切换率,减缓磁共振成像时间的问题。方法 采用梯度电感最小化和梯度磁场自屏蔽等约束条件,结合目标场点法建立数学模型,求解双平面梯度线圈的绕线分布,以减小梯度线圈的磁场能量,降低涡流等对梯度线圈性能的影响。同时,为缩短数值计算工具MATLAB软件对以上复杂数学模型矩阵求解的运算时间,采用图形处理单元(Graphic Processing Unit,简记为GPU)加速算法实现梯度线圈的设计计算。结果 采用最小化梯度电感,能有效降低涡流影响,实现磁场自屏蔽,具有良好的梯度切换率,改善了梯度线圈的整体性能。结论 本文的双平面梯度线圈新型目标场点法算法有效可行,能够实现梯度电感最小化和梯度磁场的自屏蔽的梯度线圈设计。     

磁共振脉冲设计优化算法研究进展

脉冲优化设计是进行磁共振波谱分析、磁共振成像等的关键技术,目前常用的优化工具是最优控制理论。本文首先简要介绍MR最优控制理论,在此基础上对梯度上升算法、Krotov算法、伪谱法等三种常用的脉冲优化算法进行介绍和对比,并针对实际应用中存在的带宽、射频场不均匀性、弛豫等影响因素给出相应的算法扩展。     

MR3D-STIR-SPACE序列增强扫描诊断神经纤维瘤病一例

<正>神经纤维瘤病(neurofibromatosis)为一类中、外胚层发育异常所致的常染色体显性遗传病,具有家族性和遗传性发病倾向[1],并非真性肿瘤,而多属斑痣性错构瘤[2]。3D-STIR-SPACE序列增强扫描即重T2脂肪抑制技术,成像基础为平面回波成像(echo plannar imaging,EPI)、敏感编码并行采集技术(sensitivity eneoded,SENSE)、短TI反转     

抗CCP抗体、抗MCV抗体和RF抗体联合检测与磁共振成像诊断类风湿关节炎相关性分析

目的分析抗环瓜氨酸肽抗体(抗CCP抗体)、抗突变型瓜氨酸波形蛋白抗体(抗MCV抗体)和类风湿因子(RF)联合检测结果与磁成振成像结果相关性,探讨3种抗体在类风湿骨侵蚀性关节炎亚型诊断中的临床意义。方法收集300例类风湿关节炎患者,300例健康体检者血清标本,检测其抗CCP抗体、抗MCV抗体和RF,并行磁共振成像检查,对结果进行相关性分析。结果抗CCP抗体、抗MCV抗体和RF联合检测的灵敏度为92.0%,行磁共振成像检查灵敏度为97.3%,二者检查结果相关性良好,Kappa系数等于0.78。结论抗CCP抗体、抗MCV抗体和RF都是诊断类风湿侵蚀性关节炎亚型的良好指标,3种指标联合检测与磁共振成像具有中度一致性,可以替代磁共振成像检查,减轻患者经济负担,广泛应用于RA筛查之中。     

Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation

ContextThere has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the inverse problem, also known as the “image reconstruction from projections” problem.AimIn this paper, we propose a modified Simultaneous Algebraic Reconstruction Technique (SART) algorithm to improve the quality of image reconstruction by incorporating total variation (TV) minimization into the iterative SART algorithm.MethodologyThe SART updates the estimated image by forward projecting the initial image onto the sinogram space. Then, the difference between the estimated sinogram and the given sinogram is back-projected onto the image domain. This difference is then subtracted from the initial image to obtain a corrected image. Fast total variation (FTV) minimization is applied to the image obtained in the SART step. The second step is the result obtained from the previous FTV update. The SART and the FTV minimization steps run iteratively in an alternating manner. Fifty iterations were applied to the SART algorithm used in each of the regularization-based methods. In addition to the conventional SART algorithm, spatial smoothing was used to enhance the quality of the image. All images were sized at 128 × 128 pixels.ResultsThe proposed algorithm successfully accomplished edge preservation. A detailed scrutiny revealed that the reconstruction algorithms differed; for example, the SART and the proposed FTV-SART algorithm effectively preserved the hot lesion edges, whereas artifacts and deviations were more likely to occur in the ART algorithm than in the other algorithms.ConclusionsCompared to the standard SART, the proposed algorithm is more robust in removing background noise while preserving edges to suppress the existent image artifacts. The quality measurements and visual inspections show a significant improvement in image quality compared to the conventional SART and Algebraic Reconstruction Technique (ART) algorithms.     

基于GA与模糊连接的冠状动脉造影图像分割

目的 针对模糊连接图像分割方法在实现上的困难,提出了一种基于遗传算法的模糊连接分割方法.方法 首先把路径强度作为遗传算法的寻优条件,从而得到图像中各点到种子点间的最优路径,其强度即该点的模糊连接度.然后,以各点的模糊连接度作为其灰度值,形成目标和背景不交叉的新图,最后用阈值法把目标物体提取出来.结果 实验结果表明,将该方法应用于冠状动脉造影图像的血管分割,能准确快速地将血管从背景中分割出来.结论 该分割方法较好地满足了临床冠心病诊断的要求.     

星形算法对心尖四腔切面左心室内膜全自动勾画的初步研究

目的初步测试一种叫做星形算法的全自动超声边界探测方法的实用价值.方法星形算法采用了从左心室腔内向四周作放射形探测的心内膜探测方法,并且运用了以平均相邻距离和局部平均边界灰度为构件的代价函数来勾画边界.结果本文用八组带有各种质量问题的舒张末期和收缩末期心尖四腔切面图像对星形算法进行测试,与手工勾画的心内膜相比,星形算法勾画边界的平均半径误差率为12.90%,所围平均面积误差率为10.93%.结论星形算法能够在临床条件下实现心内膜的全自动勾画.     

Real-Time Monitoring of High-Intensity Focused Ultrasound Thermal Therapy Using the Manifold Learning Method

High-intensity focused ultrasound (HIFU) induces thermal lesions by increasing the tissue temperature in a tight focal region. The main ultrasound imaging techniques currently used to monitor HIFU treatment are standard pulse-echo B-mode ultrasound imaging, ultrasound temperature estimation and elastography-based methods. The present study was carried out on ex vivo animal tissue samples, in which backscattered radiofrequency (RF) signals were acquired in real time at time instances before, during and after HIFU treatment. The manifold learning algorithm, a non-linear dimensionality reduction method, was applied to RF signals which construct B-mode images to detect the HIFU-induced changes among the image frames obtained during HIFU treatment. In this approach, the embedded non-linear information in the region of interest of sequential images is represented in a 2-D manifold with the Isomap algorithm, and each image is depicted as a point on the reconstructed manifold. Four distinct regions are chosen in the manifold corresponding to the four phases of HIFU treatment (before HIFU treatment, during HIFU treatment, immediately after HIFU treatment and 10-min after HIFU treatment). It was found that disorganization of the points is achieved by increasing the acoustic power, and if the thermal lesion has been formed, the regions of points related to pre- and post-HIFU significantly differ. Moreover, the manifold embedding was repeated on 2-D moving windows in RF data envelopes related to pre- and post-HIFU exposure data frames. It was concluded that if mean values of the points related to pre- and post-exposure frames in the reconstructed manifold are estimated, and if the Euclidean distance between these two mean values is calculated and the sliding window is moved and this procedure is repeated for the whole image, a new image based on the Euclidean distance can be formed in which the HIFU thermal lesion is detectable.     

基于SAPSO优化三维Otsu方法的医学图像分割算法

医学图像具有内容丰富多样、特征丰富、多尺度等特性,因此对医学图像的分割比一般图像的难度更大.针对上述问题,提出了基于改进粒子群优化三维Otsu方法的医学图像分割算法.由于三维Otsu方法计算量大,采用粒了群优化算法来搜索阈值向量,每个粒子代表一个可行的阈值向量,通过粒子群之间的协作来获得最优阈值.由于粒子群优化算法容易陷入局部最优解的的缺点,提出了模拟退火的粒子群优化方法,使其能够快速准确得到整体最佳解,还能保持粒子群算法求解速度快的优点.最后通过仿真实验得出了结论表明,所提出的方法不仅能得到理想的结果,而且计算量人大减少.     

基于Mallat金字塔算法的小波的局部图像重建

本文利用Mallat金字塔算法对Shepp-Logan图进行局部重建,利用小波所具有的多尺度特性和良好的局部特性,能够仅用局部投影数据重建出感兴趣的局部区域,由此不仅降低了X射线的辐射剂量,而且缩短了成像时间.     

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

目的　介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。方法　首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－ 均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果　模糊Ｋ－ 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论　利用模糊Ｋ－ 均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

关于一个全自动勾画超声左心室内膜算法的初步研究

目的 :本文叙述一个新的全自动勾画心内膜算法的初步研究情况 ,这个算法将是全自动心功能定量分析的基础。该算法应用中心起源的形心搜索、用改进的 Sobel算子进行的放射形边界探测和依据相邻离心距差进行的边界点修正。方法 :我们用自动勾画 1 2张超声图像 (其中 6张高质量的图像 ,6张低质量的图像 )中心内膜边界的方法来检测该算法。结果 :在 1 2张超声图像中我们的算法成功地勾画出 7张图像中的心内膜 ,其中包括一张低质量的图像。结论 :虽然该算法还需要改进 ,但 58%的勾画成功率已相当令人满意     

MR image reconstruction based on framelets and nonlocal total variation using split Bregman method

Purpose

An efficient algorithm for magnetic resonance (MR) image reconstruction is needed, especially when sparse sampling is employed to accelerate data acquisition. The aim of this paper is to solve the sparse MRI problem based on nonlocal total variation (NLTV) and framelet sparsity using the split Bregman algorithm. A new method was developed and tested in a variety of MR image acquisitions.

Methods

The proposed method minimizes a linear combination of NLTV, least square data fitting and framelet terms to reconstruct the MR images from undersampled $k$ -space data. The NLTV and framelet sparsity are taken as the $L_{1}$ -regularization functional and solved by using the split Bregman method. Experiments were conducted to compare the proposed algorithm with several different reconstruction methods, including the operator splitting algorithm, variable splitting method, composite splitting algorithm and its accelerated version called the fast composite splitting algorithm. A detailed evaluation study was done on the reconstruction of MR images which represent varying degrees of object structural complexity. Both qualitative visualization-based and quantitative metric-based evaluations were done.

Results

Numerical results on various data corresponding to different sampling rates showed the advantages of the new method in preserving geometrical features, textures and fine structures. The proposed algorithm was compared with previous methods in terms of the reconstruction accuracy and computational complexity with favorable results.

Conclusion

An efficient new algorithm was developed for compressed MR image reconstruction based on NLTV and framelet sparsity. The algorithm effectively solves a hybrid regularizer based on framelet sparsity and NLTV using the split Bregman method. NLTV makes the recovered image quality sharper by preserving the edges or boundaries more accurately, and framelets often improve image quality. The comparison with alternative method yielded results that demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.