基于图像非线性配准的MRI几何失真校正

采用图像非线性配准的方法,对MRI存在的几何失真进行校正,先选取与MRI具有共同解剖结构且基本无几何失真的CT图像,作为配准校正的目标基准图像,将MRI与CT图像进行刚体配准,然后采用人工选点的方法,寻找CT图像和MRI中的对应标记点集,用改进的弹体样条函数进行全局非线性配准.结果表明:弹体样条变换具有较强的非线性校正能力,较好地实现了二维MRI的几何失真校正.我们采用的变换模型有效可行,并可用于三维失真图像的校正.     

基于互信息量的医学图像自动弹性配准

本研究采取一种基于互信息量的分级自动选点方法选取对应特征点集。得到对应点集后 ,利用改进的薄板样条插值方法来进行弹性配准。用此方法 ,得到了好的配准效果     

基于深度学习的2D/3D医学图像配准研究

2D/3D配准在临床诊断和手术导航规划中有着广泛的应用,可解决医学图像领域中不同维度图像存在信息缺失的问题,能辅助医生在术中精准定位患者的病灶。常规的2D/3D配准方法主要依赖于图像的灰度进行配准,但非常耗时,不利于临床实时性的需求,并且配准过程中容易陷入局部最优值。提出用深度学习的方法来解决2D/3D医学图像配准问题。采用一个基于深度学习的卷积神经网络,通过网络对数字影像重建技术(DRR)进行训练并自动学习图像特征,预测X光图像所对应的参数,从而实现配准。以人体骨盆的模型骨为实验对象,根据骨盆的CT数据生成36000张DRR图像作为训练集,同时通过C臂采集模型骨的50张X光图像作为验证。结果显示,深度学习算法在相关系数、归一化互信息、欧式距离3个精度评价指标上的测试值分别为0.82±0.07、0.32±0.03、61.56±10.91,而常规2D/3D算法对应的测试值分别为0.79±0.07、0.29±0.03、37.92±7.24,说明深度学习算法的配准精度优于常规2D/3D算法的配准精度,且不存在陷入局部最优值的问题。同时,深度学习的配准时间约为0.03s,远低于常规2D/3D配准的时间,可满足临床对于实时配准的需求,未来将进一步开展临床数据的2D/3D配准研究。     

用无约束优化薄板样条实现平滑的医学图像弹性配准

医学图像弹性配准是医学图像处理的一个重要研究方向。目前采用的方法多是手动选择对应标记点，然后用薄板样条插值方法计算配准变换。由于对应点的选取总是存在误差，所以配准的准确性受到影响，而且手动选点操作繁杂、耗时大。为此，我们根据最优化理论，改进了薄板样条插值方法，并在此基础上采用了一种自动标记点选择方法。将这两者结合，我们得到了一种自动、准确、鲁棒性好的配准方法，运用此方法进行医学图像的弹性配准，得到了更好的结果。     

寰椎骨折前路复位内固定钢板置钉参数研究

目的　明确寰椎骨折前路复位内固定钢板寰椎侧块置钉可行性及置钉技术参数。 方法　用Mimics软件,对40例被检查者的CT数据进行三维重建,解剖测量,并模拟置入寰椎侧块螺钉,测量并获得置钉的技术参数。 结果　椎动脉孔内侧壁距离中线23.2 mm。寰椎后弓与寰椎侧块移行处内侧壁距离中线距离13.2 mm。寰椎侧块上位螺钉置钉点距离上关节面前缘6.2 mm,距离寰椎中线20.0 mm。上位螺钉长21.5 mm,于矢状面上成角范围向上1.5°～向下11.6°。寰椎侧块下位螺钉置钉点距离下关节面前缘8.9 mm,长15.2 mm,最大下倾角为20.7°,距离椎动脉孔内侧壁1.9 mm。距中线17.6~23.2 mm的侧块为JeRP钢板侧块螺钉置入的相对安全区域。 结论　寰椎侧块置入上下位螺钉具备可行性。置钉点及钉道方向必须根据患者术前的三维CT数据做最终的决定。上位螺钉置钉角度应宁下勿上,下位螺钉置钉角度应宁内勿外。     

亚像素断层图像配准数据集的建立

目的针对数字化冰冻铣切断层图像的特点,探讨一种实用的高精度图像配准方法,建立基于数字化断层图像的亚像素级配准数据集。方法采用冰冻铣切技术获取成年男性头颈标本的冰冻连续断层图像,在M atlab软件中自动提取定标点图像特征,采用基于2点的刚体变换算法实现图像的自动配准。结果配准后图像定标点与基准配准点的误差小于1个像素,达到亚像素水平。结论采用外定标的图像配准算法可建立亚像素级的配准数据集,定位标记物的准确识别是获得亚像素级配准数据集的保证。     

脊柱导航手术机器人寰枢椎椎弓根的双置入方法

背景：寰枢椎椎弓根内固定具有良好的生物力学效果,但因其椎弓根容积小、个体差异大,与脊髓、椎动脉及神经根等组织毗邻等原因使这一内固定技术的推广应用受到限制,寰枢椎椎弓根置入精度有待进一步提高。 目的：探索自主研制的脊柱导航手术机器人进行寰枢椎椎弓根双置入的原理,以建立一种精度更高、更为安全且简便易行的新方法。 方法：置入前在头尾侧等分椎弓根的寰枢椎CT剖面图像上,于正中矢状线两侧、椎骨后部弧形骨线上各选取一点,其联线构成一条横线;位于该横线与切过寰枢椎后部最远点的水平线间的正中矢状线构成一条竖线,这样就在寰枢椎椎骨上即体内设置了一条长度已知且相互垂直的横线与竖线。术中在双置入机器手两定位杆尖端间和线配准器上各设定一条相互垂直的横线和竖线,即体外横线和竖线,其长度分别与体内横线和竖线相等。通过X射线侧位透视,将体内和体外的横线与竖线配准,并使两定位杆的尖端紧压寰椎后弓骨面,从而确定寰枢椎椎骨的正中矢状面、冠状面及水平面。设定双置入机器手两导针中心轴线间的距离与左右两置入点间的距离相等,且其中心轴线与正中面的夹角与术前测量的椎弓根中心轴线与正中矢状线的夹角相等,实时动态监测下,脊柱导航手术机器人沿椎弓根中心轴线准确双侧置入。 结果与结论：应用脊柱导航手术机器人行寰枢椎椎弓根双置入,当体内、外的横线及竖线配准及双置入机器手两定位杆的尖端紧密接触弧形骨面时,两导针的尖端只能落在左右椎弓根的置入点上。实时动态监测下,可保证沿寰枢椎左右椎弓根中心轴线准确双置入。     

三维CT重建参照下徒手寰枢椎椎弓根螺钉置入内固定治疗外伤性寰枢椎不稳

背景：寰枢椎椎弓根螺钉内固定在三维CT重建参照下比传统方法能提高置钉的准确率,减少置入并发症。 目的：探讨以寰枢椎三维CT重建为参照,进行寰枢椎椎弓根螺钉内固定治疗外伤性寰枢椎不稳的方法,明确其手术指导意义以及临床治疗效果。　 方法：对30例因外伤导致寰枢椎不稳需行寰枢椎椎弓根螺钉内固定治疗的患者内固定置入前行三维CT重建。 结果与结论：与螺钉置入前设计钉道内倾度、设计钉道测得进钉点与中线的距离比较,经C1、C2椎弓根螺钉实际钉道内倾角及进钉点与中线的距离差异均无显著性意义。30例患者观察到的C1后弓及C2椎弓表面解剖特征与置入前CT容积再现的影像一致。说明根据三维CT重建图像为参照进行寰枢椎椎弓根螺钉内固定,徒手置入,节省时间,并减少术中接收X线辐射,个性化置钉,精确、安全性高、疗效优良。     

寰椎椎弓根螺钉技术的剖而解剖和三维CT应用研究

目的:对寰椎进行剖面解剖和三维CT测量.为临床提供寰椎椎弓根螺钉技术的解剖学和三维CT参数.方法:采用游标卡尺和量们器测量30例60侧C1湿骨剖面标本,采用GE LightSpeed 16 Pro螺旋CT操作软件(ADW4.2)测量30例60侧30～50岁正常人的C1-三维CT图片结果:剖面解剖学和3D-CT的测量结果分别为:寰椎侧块宽度为(10.42±1.50)mm、(10.1±1.4)mm;侧块长度为(21.33±1.06)mm、(21.5±1.3)mm;侧块高度为(10.22±0.80)mm、(10.3±1.1)mm;椎弓根中点至侧块前缘的距离为(27.98±1.32)mm、(27.8±1.2)mm;中线至寰椎椎管外缘的距离为(13.81±1.36)mm、(14.5±1.1)mm;中线至寰椎椎弓根中点的距离为(18.02±1.64)mm、(18.6±1.1)mm;中线至寰椎横突孔内壁的距离为(22.06±1.90)mm、(22.1±1.7)mm;椎管外缘至横突孔内肇的距离为(8.22±1.62)mm、(7.6±1.4)mm;寰椎后弓最薄处的宽度为(9.60±1.44)mm、(9.7±1.3)mm;高度为(4.60±0.11)mm、(4.4±0.8)mm.其中17侧(18侧)的寰椎后弓最薄处高度<4 mm.占28.3%(30%);寰椎侧块角为(24.6±1.1)°、(24.8±1.5)°两者的符个参数均无显著差异(P0.05).结论:剖面解剖学和三维CT的寰椎测量一致,均可为骨科医师提供寰椎椎弓根螺钉技术的可行性、进针点、进针方向和进针深度,但三维CT可提供具体、个体化的依据.     

基于统计形状模型的三维足形状建模

目的 构建足的三维统计形状模型(statistical shape model,SSM),揭示足形状的三维变化特征.方法 50名中国青年男性正常足数据用于三维统计形状建模,通过足面网格配准、点对齐及主成分(principal component,PC)降维分析等步骤,获得平均足形态及在各PC维度标准差等参数化的足形态....     

数据驱动的三叉神经纤维束自动分割算法

目前三叉神经的纤维跟踪成像过程中普遍存在人工依赖性问题,主要包括人工绘制感兴趣区域(ROI)及手动筛选目标纤维束,导致结果的不确定性和数据误差。针对此类问题,提出一种数据驱动的三叉神经纤维自动分割算法。利用多组大脑样本的纤维数据,建立数据驱动的纤维聚类图谱,实现新样本纤维数据的自动分割,直接得到三叉神经纤维束。在实验中,选择25组青年健康人的数据作为样本数据。首先,利用FSL软件分割工具提取脑干作为ROI,进行确定性纤维跟踪。其次,通过对20组纤维数据进行多样本配准和谱聚类,创建数据驱动的纤维聚类图谱。根据三叉神经细小的特点,在建立纤维图谱过程中,通过对脑干纤维束进行二次分类来标注三叉神经纤维束。最后,选择5组青年健康人的新样本数据,将其脑干纤维数据应用纤维图谱自动分割得到三叉神经纤维束,并计算同一样本数据的自动分割结果与手动分割结果之间的加权Dice系数。结果显示,所提出的方法成功分割5组数据的三叉神经纤维束,而传统人工方法成功识别4组三叉神经纤维束,两者结果之间的加权Dice系数分别为0.865,0.939,0.824,0.942。该方法可以有效避免人为因素的影响,提高神经外科医生与颅神经研究者的工作效率。     

Automated Bone Segmentation and Surface Evaluation of a Small Animal Model of Post-Traumatic Osteoarthritis

MicroCT imaging allows for noninvasive microstructural evaluation of mineralized bone tissue, and is essential in studies of small animal models of bone and joint diseases. Automatic segmentation and evaluation of articular surfaces is challenging. Here, we present a novel method to create knee joint surface models, for the evaluation of PTOA-related joint changes in the rat using an atlas-based diffeomorphic registration to automatically isolate bone from surrounding tissues. As validation, two independent raters manually segment datasets and the resulting segmentations were compared to our novel automatic segmentation process. Data were evaluated using label map volumes, overlap metrics, Euclidean distance mapping, and a time trial. Intraclass correlation coefficients were calculated to compare methods, and were greater than 0.90. Total overlap, union overlap, and mean overlap were calculated to compare the automatic and manual methods and ranged from 0.85 to 0.99. A Euclidean distance comparison was also performed and showed no measurable difference between manual and automatic segmentations. Furthermore, our new method was 18 times faster than manual segmentation. Overall, this study describes a reliable, accurate, and automatic segmentation method for mineralized knee structures from microCT images, and will allow for efficient assessment of bony changes in small animal models of PTOA.     

MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera

This paper introduces a mouse atlas registration system (MARS), composed of a stationary top-view x-ray projector and a side-view optical camera, coupled to a mouse atlas registration algorithm. This system uses the x-ray and optical images to guide a fully automatic co-registration of a mouse atlas with each subject, in order to provide anatomical reference for small animal molecular imaging systems such as positron emission tomography (PET). To facilitate the registration, a statistical atlas that accounts for inter-subject anatomical variations was constructed based on 83 organ-labeled mouse micro-computed tomography (CT) images. The statistical shape model and conditional Gaussian model techniques were used to register the atlas with the x-ray image and optical photo. The accuracy of the atlas registration was evaluated by comparing the registered atlas with the organ-labeled micro-CT images of the test subjects. The results showed excellent registration accuracy of the whole-body region, and good accuracy for the brain, liver, heart, lungs and kidneys. In its implementation, the MARS was integrated with a preclinical PET scanner to deliver combined PET/MARS imaging, and to facilitate atlas-assisted analysis of the preclinical PET images.     

基于VIP-Man图谱配准的腹部器官分割方法

提出了一种基于图谱配准的腹部器官分割方法.首先将一套预标记图谱向个体图像进行配准,建立二者之间器官的基本对应关系,同时完成对感兴趣器官的识别,其中配准包含全局配准和器官配准.然后,借助已配准的图谱,采用模糊连接方法对感兴趣器官进行分割.腹PCT和MR实验测试结果证明:这种方法实现了模糊连接分割方法中各项参数的自动指定,减轻了人工负担,提高了结果的可靠性.     

基于图谱的肝脏CT三维自动分割研究

目的在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤。由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一。方法本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序。首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准。配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积。结果评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部（主要在肝脏尖端）出现较大的误差。结论该方法分割临床肝脏CT图像具有可行性。     

Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information

An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the segmentation error remains below 1 mm in 50% of the cases and below 1.5 mm in 75% of the cases.     

Validation of a semi‐automatic co‐registration of MRI scans in patients with brain tumors during treatment follow‐up

There is an expanding research interest in high‐grade gliomas because of their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment. We have developed a two‐stage semi‐automatic method to co‐register MRI scans at different time points before and after surgical and adjuvant treatment of high‐grade gliomas. This two‐stage co‐registration includes a linear co‐registration of the semi‐automatically derived mask of the preoperative contrast‐enhancing area or postoperative resection cavity, brain contour and ventricles between different time points. The resulting transformation matrix was then applied in a non‐linear manner to co‐register conventional contrast‐enhanced T₁‐weighted images. Targeted registration errors were calculated and compared with linear and non‐linear co‐registered images. Targeted registration errors were smaller for the semi‐automatic non‐linear co‐registration compared with both the non‐linear and linear co‐registered images. This was further visualized using a three‐dimensional structural similarity method. The semi‐automatic non‐linear co‐registration allowed for optimal correction of the variable brain shift at different time points as evaluated by the minimal targeted registration error. This proposed method allows for the accurate evaluation of the treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients. Copyright © 2016 John Wiley & Sons, Ltd.     

基于Hausdorff距离的CARTO电解剖图与CT曲面配准

我们提出一种全自动、高精度的CARTO电解剖图(EAM)与CT曲面的配准算法。算法首先以基于主轴的方法粗配准EAM与CT曲面,然后再以基于Hausdorff距离的方法进一步实现EAM与CT曲面精配准。实验结果表明,相对Carto-Merge配准软件以及随机配准算法,基于Hausdorff距离的配准算法能获得更稳定且更精确的EAM与CT曲面配准效果,同时算法完全自动,基于Hausdorff距离的EAM与CT曲面配准算法能很好地满足房颤消融手术的临床应用需求。     

Automatic registration of megavoltage to kilovoltage CT images in helical tomotherapy: an evaluation of the setup verification process for the special case of a rigid head phantom

Precise daily target localization is necessary to achieve highly conformal radiation delivery. In helical tomotherapy, setup verification may be accomplished just prior to delivering each fraction by acquiring a megavoltage CT scan of the patient in the treatment position. This daily image set may be manually or automatically registered to the image set on which the treatment plan was calculated, in order to determine any needed adjustments. The system was tested by acquiring 104 MVCT scans of an anthropomorphic head phantom to which translational displacements had been introduced with respect to the planning image set. Registration results were compared against an independent, optically guided positioning system. The total experimental uncertainty was within approximately 1 mm. Although the registration of phantom images is not fully analogous to the registration of patient images, this study confirms that the system is capable of phantom localization with sub-voxel accuracy. In seven registration problems considered, expert human observers were able to perform manual registrations with comparable or inferior accuracy to automatic registration by mutual information. The time to compute an automatic registration is considerably shorter than the time required for manual registration. However, human evaluation of automatic results is necessary in order to identify occasional outliers, and to ensure that the registration is clinically acceptable, especially in the case of deformable patient anatomy.     

基于最大互信息的人脑多模图像快速配准算法

对脑图谱开发过程中来源于不同成像设备的多模图像进行配准。对预处理后的数码图像和MRI图像,首先提取图像的轮廓,采用基于轮廓的力矩主轴法计算初始平移量和旋转量,然后设定初始缩放系数,将此初始配准参数作为改进单纯形法的初始参数,以互信息作为相似性测度迭代搜索,使互信息最大,从而实现最佳配准。结果表明本算法不需要人为预调整待配准图像的分辨率,自动化程度高,配准速度快,精度较高,能够满足脑图谱开发过程中的多模图像配准要求。