一种MR图像灰度偏差场的修正方法

背景:目前的核磁共振图像不可避免会有灰度偏差场存在,会对医学图像的计算机数字处理产生非常不利的影响,如分割、配准、量化等.因此对于这种不利的空间密度变化的修正成为许多图像分析任务必要的预处理步骤.目的:提出一种以图像数据为基础不依赖于模板的复杂图像信号偏差场的修正方法.方法:数据是来源于首都医科大学复兴医院放射影像科,采集于美国GE公司核磁共振设备,所有图像来源于志愿者的核磁共振图像.成像视野250 mm×250 mm,矩阵256×256,层厚1 mm.对核磁共振图像处理目标进行锁定的Mask方法,并借助信息熵的方法实现对核磁共振图像灰度偏差场的有效修正. 结果与结论:通过核磁共振图像实验,证明该方法可以有效准确地修正核磁共振图像灰度偏差场的伪影.实验结果为核磁共振图像计算机分析提供了一种有效的修正灰度偏差场方法,提高了图像分析的准确性和鲁棒性.     

超声内镜联合Photoshop对食管间质瘤与 平滑肌瘤鉴别的运用

目的 运用Photoshop软件分析食管间质瘤与平滑肌瘤的超声内镜影像,探讨其鉴别两疾病的价值。 方法 利用Photoshop软件分析经病理及免疫组化确诊的9例食管间质瘤和42例食管平滑肌瘤的超声内镜图像,分别测定间质瘤和平滑肌瘤瘤体感兴趣区的灰度平均值（表示回声强度）与灰度标准差平均值（表示回声均匀度）。 结果 9例食管间质瘤超声内镜图像灰度平均值的均数为73.47,42例食管平滑肌瘤图像灰度平均值均数为58.08,两者差异有显著性（t =4.367,P <0.01）;9例食管间质瘤超声内镜图像灰度标准差的均数为21.12,42例食管平滑肌瘤图像灰度标准差的均数为13.93,两者差异有显著性（t =2.894,P <0.01）。 结论 在超声内镜下食管间质瘤回声强度总体上高于食管平滑肌瘤,而食管平滑肌瘤的回声均匀性优于食管间质瘤,通过Photoshop软件分析超声内镜图像的灰度平均值及灰度标准差可以有助于鉴别食管间质瘤和食管平滑肌瘤。     

SPM软件包头动校正原理的分析与评价

背景:头动校正是功能磁共振图像处理的一个重要步骤,也是目前应用广泛的SPM软件的第一步,其效果直接关系到结果的可靠性.目的:分析SPM软件包头动校正处理的基本原理和关键算法,包括刚体变换、高斯牛顿迭代算法和B样条插值算法.方法:使用SPM2软件包对一组实际数据进行了处理,并结合头动校正的原理,提出了头动校正效果的评价方法:残差图与残差平方和.结果与结论:使用高斯牛顿迭代算法得到优化的刚体变换参数,用B样条插值算法对须校正的图像进行重采样得到校正后图像,实现图像序列的头动校正.实验结果表明,评价指标能较好衡量算法效果.     

年龄对肌肉超声图像纹理特征的影响

目的:利用肌骨超声获得年轻人和老年人肌肉图像,分析肌肉形态学差异,并结合图像处理技术分析肌肉纹理特征随年龄的变化。方法:本试验于2018年5-7月招募健康成年志愿者50例,根据年龄分为青年组(<30岁)和老年组(≥60岁),青年组22例,老年组28例。使用肌骨超声获得两组志愿者内侧腓肠肌横切和纵切图像,直接测量肌肉厚度和羽状角。将所获得的图像进行兴趣区域的选择并从中提取9个纹理特征,包括基于灰度直方图的灰度均值和灰度方差;基于灰度共生矩阵的对比度和同质性;基于灰度梯度共生矩阵的灰度熵;基于游程长度矩阵的灰度不均匀性、游程长度不均匀性、低灰度游程优势和高灰度游程优势。结果:肌肉形态学上,老年组内侧腓肠肌肌肉厚度显著小于青年组(P<0.05),两组间羽状角无显著差异(P>0.05)。肌肉纹理上,与青年组相比,老年组的灰度方差、对比度、灰度熵、灰度不均匀性、低灰度游程优势显著降低(P<0.05);同质性、游程长度不均匀性显著增加(P<0.05)。两组间的灰度均值、高灰度游程优势无显著差异。结论:通过分析肌肉超声图像发现老年人有更大的同质性、游程长度不均匀性和更小的灰度方差、对比度、灰度熵、灰度不均匀性及低灰度游程优势。肌肉超声图像纹理分析技术能够反映肌肉内部组织随年龄增长的不利改变,可作为一种研究年龄相关的肌肉变化的生物学标志物。     

低场磁共振水成像结合常规磁共振成像在胆道及泌尿系统疾病诊断中的应用

目的评价低场磁共振水成像结合常规磁共振成像（MRI）在胆道及泌尿系统疾病诊断中的临床价值。方法回顾性分析31例临床怀疑患有胆道、泌尿系统疾病患者的低场（0．2T）磁共振胰胆管成像（MRCP）、磁共振尿路成像（MRU）及常规MRI图像。结果低场MRCP、MRU结合常规MRI对胆道、泌尿系统性疾病的定位诊断符合率为100％,定性诊断总符合率为96．5％。结论低场磁共振水成像结合常规MRI在胆道及泌尿系统疾病诊断中的诊断是非常有效和准确的方法。     

基于脑梗死大鼠MR图像熵的偏差场拟合方法

背景:目前随着高场强MR设备的不断出现,由于受RF线圈等因素的影响,伪影成为MR图像不可避免的问题.在MR图像上呈现出无规律的、缓慢的灰度密度变化,相当于在增益场上叠加了一个偏差场.这不仅降低了MR图像的质量也严重干扰了数字图像分割和量化处理等方法的运用.目的:提出一种以图像数据为基础并基于熵的复杂信号偏差场的修正方法.方法:根据大鼠脑立体定位图谱,对脑梗死电凝模型大鼠进行冠状位MRI检查.采用GE Signa 1.5T磁共振扫描仪,3inch 表面线圈.层厚6 mm,间隔1 mm(3D T2WI采用层厚2 mm,间隔O.5 mm).该方法是校正场寻优的过程,优化过程以二维信息熵作为尺度.算法的目的是利用最优校正场,使带有偏差的MR图像去偏差,复原真实图像.结果与结论:实验以真实和模拟数据对该方法进行实验,与传统方法相比,经该方法去偏差场后,其各组织在直方图中的分布和对比更加明显,更易于进行组织分割和组织提取.通过计算机时间比较,文章提出的方法在低密度Delaunay三角剖分下,比传统同态滤波方法省时;但在高密度Delaunay三角剖分下,比传统方法费时.     

一种快速校正CT环形伪影的方法

背景：环形伪影严重影响了CT图像质量,对图像后处理造成困难以及容易造成误诊断。目前去除环形伪影必不可少。目的：去除CT重建图像中的环形伪影,提高CT图像质量以及后续处理和量化分析的精度,便于诊断。方法：首先把含环形伪影的CT图像进行线性变换,将灰度图像转换成浮点类型的图像。接着由直角坐标变换到极坐标,这样原来的环形伪影就被变换成线形伪影。设计多维滤波器,计算每个象素滤波后均值以及方差,通过与闽值比较确定伪影范围。最后通过对伪影范围进行修正以及对图像进行坐标变换,变为灰度图像。结果与结论：通过Matlab7.0软件设计程序,处理含环形伪影的CT图像。实验表明,此方法能有效快速地校正CT环形伪影,是一种属于图像后处理的校正方法。     

获取医学灰度图像轮廓图的算法

目的:提出一种改进的形态学边缘检测算子,以获取医学灰度图像轮廓图,并保持边缘的平滑性,并与传统的边缘提取方法进行比较。方法:图像边缘检测通常是以类似于素描图的图像表达出物体的要素和特征,其任务是使图像边缘准确定位和抑制噪声。试验采用3×3的模板作为结构元素对原图像进行处理,利用基于数学形态学的方法,用形态运算膨胀、腐蚀、开、闭等变换以及它们的组合及灰度切片的方法获取质量好的医学轮廓图像,并与用Sobel算子方法和Roberts算子方法获得的轮廓图像进行比较。结果:试验结果表明,Sobel和Roberts等算子不能全面检测出边缘,且边缘模糊。采用复合型数学形态学算子与灰度切片结合的算法获得的轮廓图边缘连续和完整,断点少,且轮廓周围的灰度已进行合并具有更丰富的细节,相对于常用的微分算子和形态学边缘梯度算子更能有效地滤除噪声,这一方法对噪声医学图像边缘的提取效果更好。结论:采用复合型数学形态学算子与灰度切片结合的算法从原始图像获取医学轮廓图像效果好,优于传统的边缘提取方法。     

一种快速校正CT环形伪影的方法

背景:环形伪影严重影响了CT图像质量,对图像后处理造成困难以及容易造成误诊断.目前去除环形伪影必不可少.目的:去除CT重建图像中的环形伪影,提高CT图像质量以及后续处理和量化分析的精度,便于诊断.方法:首先把含环形伪影的CT图像进行线性变换,将灰度图像转换成浮点类型的图像.接着由直角坐标变换到极坐标,这样原来的环形伪影就被变换成线形伪影.设计多维滤波器,计算每个象素滤波后均值以及方差,通过与阈值比较确定伪影范围.最后通过对伪影范围进行修正以及对图像进行坐标变换,变为灰度图像.结果与结论:通过Matlab 7.0软件设计程序,处理含环形伪影的CT图像.实验表明,此方法能有效快速地校正CT环形伪影,是一种属于图像后处理的校正方法.     

基于Visual C++和Matlab的磁共振影像增强后处理系统

目的:随着医学影像学的飞速发展,手术导航技术的应用及脑功能等图像分析研究的不断深入,基于医学数字成像和通信标准的医学影像分析与处理也随之成为医学图像处理领域中的热点。为便于科研人员研究相应的磁共振图像局部增强等后处理算法及进行图像分析,提出一种基于VisualC 和Matlab的磁共振影像增强后处理研发平台。方法:对北京协和医院放射科2005-01/10获取的部分磁共振医学数字成像和通信图像利用灰度扩展进行全局增强,利用基于数学形态学方法进行局部增强算法的研究。在程序实现上使用Matlab引擎实现VC 和Matlab混合编程处理医学数字成像和通信图像。结果:为磁共振图像进行增强局部对比度算法研究提供了一个研发平台,实现了位图图像与医学数字成像和通信图像的数据转换接口功能。结论:处理后的图像具有更好的应用价值,为图像局部对比度增强算法的研究提供一个有效的平台。在算法研制阶段采用VC和Matlab混合编程的方法可以提高算法研究效率。     

Intensity inhomogeneity correction of multispectral MR images

Intensity inhomogeneity in MR images is an undesired phenomenon, which often hampers different steps of quantitative image analysis such as segmentation or registration. In this paper, we propose a novel fully automated method for retrospective correction of intensity inhomogeneity. The basic assumption is that inhomogeneity correction could be improved by integrating spatial and intensity information from multiple MR channels, i.e., T1, T2, and PD weighted images. Intensity inhomogeneities of such multispectral images are removed simultaneously in a four-step iterative procedure. First, the probability distribution of image intensities and corresponding spatial features is calculated. In the second step, intensity correction forces that tend to minimize joint entropy of multispectral image are estimated for all image voxels. Third, independent inhomogeneity correction fields are obtained for each channel by regularization and normalization of voxel forces, and last, corresponding partial inhomogeneity corrections are performed separately for each channel. The method was quantitatively evaluated on simulated and real MR brain images and compared to three other methods.     

ABCnet: Adversarial bias correction network for infant brain MR images

Automatic correction of intensity nonuniformity (also termed as the bias correction) is an essential step in brain MR image analysis. Existing methods are typically developed for adult brain MR images based on the assumption that the image intensities within the same brain tissue are relatively uniform. However, this assumption is not valid in infant brain MR images, due to the dynamic and regionally-heterogeneous image contrast and appearance changes, which are caused by the underlying spatiotemporally-nonuniform myelination process. Therefore, it is not appropriate to directly use existing methods to correct the infant brain MR images. In this paper, we propose an end-to-end 3D adversarial bias correction network (ABCnet), tailored for direct prediction of bias fields from the input infant brain MR images for bias correction. The “ground-truth” bias fields for training our network are carefully defined by an improved N4 method, which integrates manually-corrected tissue segmentation maps as anatomical prior knowledge. The whole network is trained alternatively by minimizing generative and adversarial losses. To handle the heterogeneous intensity changes, our generative loss includes a tissue-aware local intensity uniformity term to reduce the local intensity variation in the corrected image. Besides, it also integrates two additional terms to enhance the smoothness of the estimated bias field and to improve the robustness of the proposed method, respectively. Comprehensive experiments with different sizes of training datasets have been carried out on a total of 1492 T1w and T2w MR images from neonates, infants, and adults, respectively. Both qualitative and quantitative evaluations on simulated and real datasets consistently demonstrate the superior performance of our ABCnet in both accuracy and efficiency, compared with popularly available methods.     

Normalized mutual information based registration using k-means clustering and shading correction

In this paper the influence of intensity clustering and shading correction on mutual information based image registration is studied. Instead of the generally used equidistant re-binning, we use k-means clustering in order to achieve a more natural binning of the intensity distribution. Secondly, image inhomogeneities occurring notably in MR images can have adverse effects on the registration. We use a shading correction method in order to reduce these effects. The method is validated on clinical MR, CT and PET images, as well as synthetic MR images. It is shown that by employing clustering with inhomogeneity correction the number of misregistrations is reduced without loss of accuracy thus increasing robustness as compared to the standard non-inhomogeneity corrected and equidistant binning based registration.     

Image‐based estimation method for field inhomogeneity in brain echo‐planar images with geometric distortion using k‐space textures

Echo‐planar imaging (EPI) can suffer from geometrical distortion due to magnetic field inhomogeneity. To correct the geometric distortions in EPI, a magnetic field map is used. Our purpose was to develop a novel image‐based method for estimating the field inhomogeneity map from the distorted EPI image and T1‐weighted image of the brain using k‐space textures. Based on magnetic resonance imaging physics, our method synthesizes the distorted image to match the measured EPI image through the generating process of EPI image by updating the estimated field inhomogeneity map. The estimation process was performed to minimize the cost function, which was defined by the synthesized EPI image and the measured EPI image with geometric distortion, using an iterative conjugate gradient algorithm. The proposed method was applied to simulation and human data. To evaluate the performance of the proposed method quantitatively, we used the normalized root mean square error (NRMSE) between the ground truth and the results estimated by our proposed method. In simulation data, the values of the NRMSE between the ground truth and the estimated field inhomogeneity map were <0.08. In both simulation and human data, the estimated EPI images were very similar to input EPI images, and the NRMSE values between them were <0.09. The results of the simulated and human data demonstrated that our method produced a reasonable estimation of the field inhomogeneity map. The estimated map could be used for distortion correction in EPI images. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 45B: 142–152, 2015     

A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation

Segmentation of the prostate boundary on clinical images is useful in a large number of applications including calculation of prostate volume pre- and post-treatment, to detect extra-capsular spread, and for creating patient-specific anatomical models. Manual segmentation of the prostate boundary is, however, time consuming and subject to inter- and intra-reader variability. T2-weighted (T2-w) magnetic resonance (MR) structural imaging (MRI) and MR spectroscopy (MRS) have recently emerged as promising modalities for detection of prostate cancer in vivo. MRS data consists of spectral signals measuring relative metabolic concentrations, and the metavoxels near the prostate have distinct spectral signals from metavoxels outside the prostate. Active Shape Models (ASM's) have become very popular segmentation methods for biomedical imagery. However, ASMs require careful initialization and are extremely sensitive to model initialization. The primary contribution of this paper is a scheme to automatically initialize an ASM for prostate segmentation on endorectal in vivo multi-protocol MRI via automated identification of MR spectra that lie within the prostate. A replicated clustering scheme is employed to distinguish prostatic from extra-prostatic MR spectra in the midgland. The spatial locations of the prostate spectra so identified are used as the initial ROI for a 2D ASM. The midgland initializations are used to define a ROI that is then scaled in 3D to cover the base and apex of the prostate. A multi-feature ASM employing statistical texture features is then used to drive the edge detection instead of just image intensity information alone. Quantitative comparison with another recent ASM initialization method by Cosio showed that our scheme resulted in a superior average segmentation performance on a total of 388 2D MRI sections obtained from 32 3D endorectal in vivo patient studies. Initialization of a 2D ASM via our MRS-based clustering scheme resulted in an average overlap accuracy (true positive ratio) of 0.60, while the scheme of Cosio yielded a corresponding average accuracy of 0.56 over 388 2D MR image sections. During an ASM segmentation, using no initialization resulted in an overlap of 0.53, using the Cosio based methodology resulted in an overlap of 0.60, and using the MRS-based methodology resulted in an overlap of 0.67, with a paired Student's t-test indicating statistical significance to a high degree for all results. We also show that the final ASM segmentation result is highly correlated (as high as 0.90) to the initialization scheme.     

Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

Objective Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. Materials and methods To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes–Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.’s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. Results The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. Conclusion A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods.     

A new variational shape-from-orientation approach to correcting intensity inhomogeneities in magnetic resonance images

A new intensity inhomogeneity correction algorithm based on a variational shape-from-orientation formulation is presented. Unlike most previous methods, the proposed algorithm is fully automatic, widely applicable and very efficient. Since no prior classification knowledge about the image is assumed in the proposed algorithm, it can be applied to correct intensity inhomogeneities for a wide variety of medical images. In this paper, a finite-element method is used to model the smooth bias-field function. Orientation constraints for the bias-field function are computed at the nodal locations of the regular discretization grid away from the boundary between different class regions. The selection of reliable orientation constraints is facilitated by the goodness of fit of a first-order polynomial model to the neighborhood of each nodal location. The automatically selected orientation constraints are integrated in a regularization framework, which leads to minimization of a convex and quadratic energy function. This energy minimization is accomplished by solving a linear system with a large, sparse, symmetric and positive semi-definite stiffness matrix. We employ an adaptive preconditioned conjugate-gradient algorithm to solve the linear system very efficiently. Experimental results on a variety of magnetic resonance images are given to demonstrate the effectiveness and efficiency of the proposed algorithm.     

Susceptibility artefact correction using dynamic graph cuts: Application to neurosurgery

Echo Planar Imaging (EPI) is routinely used in diffusion and functional MR imaging due to its rapid acquisition time. However, the long readout period makes it prone to susceptibility artefacts which results in geometric and intensity distortions of the acquired image. The use of these distorted images for neuronavigation hampers the effectiveness of image-guided surgery systems as critical white matter tracts and functionally eloquent brain areas cannot be accurately localised. In this paper, we present a novel method for correction of distortions arising from susceptibility artefacts in EPI images. The proposed method combines fieldmap and image registration based correction techniques in a unified framework. A phase unwrapping algorithm is presented that can efficiently compute the B₀ magnetic field inhomogeneity map as well as the uncertainty associated with the estimated solution through the use of dynamic graph cuts. This information is fed to a subsequent image registration step to further refine the results in areas with high uncertainty. This work has been integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery and its effectiveness in correcting for geometric distortions due to susceptibility artefacts is demonstrated on EPI images acquired with an interventional MRI scanner during neurosurgery.     

Magnetic resonance-guided prostate interventions

We review our experience using an open 0.5-T magnetic resonance (MR) interventional unit to guide procedures in the prostate. This system allows access to the patient and real-time MR imaging simultaneously and has made it possible to perform prostate biopsy and brachytherapy under MR guidance. We review MR imaging of the prostate and its use in targeted therapy, and describe our use of image processing methods such as image registration to further facilitate precise targeting. We describe current developments with a robot assist system being developed to aid radioactive seed placement.     

主磁场不均匀情况下的磁共振虚拟成像初步研究

目的：设计实现一个基于布洛赫(Bloch)方程的磁共振成像模拟器,并以此仿真主磁场不均匀对成像的影响。材料与方法用C语言实现磁共振成像(magnetic resonance imaging,MRI)模拟器。成像过程中,虚拟物体各体素点的磁化强度矢量根据离散化Bloch方程递推计算得出。假定主磁场B0的不均匀性的几种模式,设定不均匀与其空间位置的关系,以此为代入条件进行模拟成像实验,并将结果与相应的实际成像对比。结果本MRI模拟器可以较好地模拟出主磁场不均匀对成像造成的影响,得到符合预期的成像结果。结论本MRI模拟器有助于工程与科研中分析主磁场不均匀对成像的影响。