基于弹性模型的CT与CARTO电解剖图像的配准算法

提出一种全自动、高精度的CARTO电解剖图与CT曲面配准算法。该算法采用了由粗到精的配准策略。粗配准部分分两步:首先采用刚体变换模型以及迭代最近点法,初次配准EAM与CT曲面;然后选择仿射变换模型,再次配准EAM与CT曲面。在粗配准的基础上,以基于B样条的自由形变模型进一步精确配准EAM与CT曲面。实验结果表明,相对临床常用的Carto-Merge配准软件,基于弹性模型的配准算法获得了远高于前者的EAM与CT曲面配准精度,配准效果稳定;同时算法完全自动,不需要任何手工介入。     

Principal axes based registration for CARTO electroanatomic map and CT surface

目的 为房颤消融提供全自动、高精度的心脏CARTO 电解剖图(electroanatomic map,EAM)与CT曲面配准算法.方法 遍历两图48种主轴对应关系,选择使得两图平均距离最小的一种,作为最终的配准结果.结果 相对临床常用的Carto-Merge影像整合软件,以及现有的随机算法,主轴配准无需任何人工操作,计算简单快速,配准精度高.结论 基于主轴的EAM与CT曲面配准算法能很好地满足临床房颤消融手术的需求.     

基于主轴的CARTO电解剖图与CT曲面配准

目的 为房颤消融提供全自动、高精度的心脏CARTO 电解剖图（electroanatomic map,EAM）与CT曲面配准算法.方法 遍历两图48种主轴对应关系,选择使得两图平均距离最小的一种,作为最终的配准结果.结果 相对临床常用的Carto-Merge影像整合软件,以及现有的随机算法,主轴配准无需任何人工操作,计算简单快速,配准精度高.结论 基于主轴的EAM与CT曲面配准算法能很好地满足临床房颤消融手术的需求.     

一种自动有效的电解剖图与CT曲面配准算法

目的为房颤消融手术提供一种自动精确的电解剖图与CT曲面配准算法。方法首先,以基于主轴的方法粗配准电解剖图与CT曲面,然后以基于迭代最近点的方法进一步精配准两图。结果采用Carto Merge、随机方法和本文提出的基于迭代最近点的方法分别逐一配准三组真实和三组模拟的电解剖图与CT曲面。相对Carto-Merge和随机方法,基于迭代最近点的方法配准结果稳定,精度最高,而且算法完全自动,无需任何手动操作。结论基于迭代最近点的电解剖图与CT曲面配准算法能很好地满足临床房颤消融手术的需求。     

标测点对基于主轴的CARTO电解剖图与CT曲面配准算法的影响

我们分析了标测点对基于主轴的CARTO电解剖图(EAM)与CT曲面配准算法的影响。在模拟的实验数据上,我们以实验的方法分别探讨标测点形变、标测点数量、标测点分布与主轴配准结果的关系。实验结果表明:主轴配准对标测点刚性形变完全鲁棒,同时也不受标测点数量的影响,但是标测点集的分布与配准精度显著相关。相对非均匀分布的点集,均匀分布的标测点集能获得更为稳定和高精度的配准结果,而且均匀分布的标测点能更好地还原真实心腔内表面。此结论对临床医生实施房颤消融手术有着重要的指导意义。     

精确锥形束CT和扇形束CT三维变形配准

自适应放疗可根据患者解剖和/或生理的变化对放疗计划进行修正。与加速器集成的锥形束CT成像装置是最普遍的在线影像获取设备。但是,由于锥形束CT固有的电子散射,重建影像的电子密度不准确,使得通常采用的基于密度的配准算法配准计划扇形束CT和在线获取的锥形束CT影像时,会产生较大的配准误差。我们通过建模图像变形配准问题为一个求解梯度距离能量泛函的极值问题,然后通过变分法和Gauss-Seidel方法获得一种新型的基于梯度信息的变形配准算法的迭代公式。该方法在迭代过程中同时考虑梯度信息的吻合和变形场的连续性,产生准确光滑的变形场。此算法迭代公式的局部特性,使其便于并行实施。通过OpenCL编程将此算法在图形处理器(GPU)上并行实施,大大缩短了配准时间。利用配准结果结合flood filling和cubic matching算法,可以快速地完成在线器官映射。算法临床数据配准结果表明,本文提出的基于梯度场的配准算法与基于密度的算法相比可以更准确地配准临床锥形束CT和扇形束CT影像。由于配准可以在很短的时间内完成,配准结果可用于在线器官映射和在线重新计划优化。     

多模态医学图像配准技术研究

目的:通过对CT和PET图像进行配准实验,得出CT和PET配准的最优插值搜索方法以及粗配准对二者配准结果的影响。方法:首先利用改进的力矩与主轴法对CT和PET图像进行粗配准,得到CT图像的平移量与旋转量。然后针对不同的插值和搜索方法实现互信息配准,并将所得平移量与旋转量作为Powell优化初值进行实验,与使用默认值的Powell优化方法进行对比。结果:实验结果表明:使用双线性插值算法时,两实验的结果基本一致;采用部分体积插值法时,基于粗配准的Powell优化配准效果优于基于默认值的方法;而使用立方卷积插值法时二者配准效果差别不大,但基于粗配准的方法时间更短。结论:基于粗配准的Powell优化方法(部分体积插值与Brent搜索)有更好的配准速度及效果。     

基于互信息医学刚性图像配准方法的研究

目的:基于互信息的配准方法是医学图像配准领域的重要方法,具有鲁棒性,精度高等优点。本文探究医学刚性图像配准的有效算法和关键技术。方法:基于互信息配准方法,利用Powell多参数算法和改进的PV插值算法,得到两幅图像之间的最大互信息和最佳配准参数。结果:二维磨牙CT图像配准实验表明,配准速度快,精度提高,验证了插值方法的有效性。结论:方法和算法可提高配准速度,能有效抑制互信息目标函数的局部极值。     

基于DRR及相似性测度的2D-3D医学图像配准算法

目的 针对术前三维电子计算机断层扫描(computed tomography,CT)图像和术中二维X线图像的配准问题提出了精确高效的全自动配准算法,使得该算法能够适应不同X线图像的风格.方法 首先通过数字重建放射影像(digitally reconstructured radiograph,DRR)技术,把CT图像转换成DRR图像,从而将CT和X线图像的配准转换成DRR图像和X线图像的配准.然后在传统的归一化互相关(normalized cross correlation,NCC)和梯度差分(gradiant difference,GD)相似性测度指标基础上提出融合NCC和GD的归一化互相关-梯度差分(normalized cross correlation?gradient difference,NG)指标,并基于NG指标计算DRR图像和X线图像的相似性.通过Powell算法迭代求取相似性测度的极值,从而获得配准矩阵.最后在人体头颅CT数据上采用"黄金标准"判断法量化配准系统的精度,并通过脊柱CT和X线配准案例验证系统在实际场景的性能.结果 基于DRR及NG相似性测度的2D/3D图像配准系统的配准距离误差为0.51 mm,角度误差为0.40°.结论 基于DRR及NG相似性测度的2D/3D图像配准算法具有较好的配准精度,能适应不同风格的二维输入图像,基于该算法的配准系统具有较好的鲁棒性.     

具有大形变特征的颅脑CT图像的非刚性配准

Demons算法是一种基于光流场模型的小形变非刚性配准算法,大形变情况下不具有拓扑保持性,将它用于颅脑CT图像配准时效果不理想。为此,本研究对它进行了改进。首先建立Demons算法目标能量函数,将形变场求解转化为目标函数优化问题;然后通过增加sKL距离作为正则项来优化目标函数,消除了形变场的不适定性,并使形变场更加光滑。对高分辨率颅脑CT图像的实验结果表明,改进算法不仅能够处理大形变问题,还能在处理大形变时通过光滑的形变场得到更精确的配准结果。     

Automatic lung nodule matching on sequential CT images

We propose an automatic segmentation and registration method that provides more efficient and robust matching of lung nodules in sequential chest computed tomography (CT) images. Our method consists of four steps. First, the lungs are extracted from chest CT images by the automatic segmentation method. Second, gross translational mismatch is corrected by optimal cube registration. This initial alignment does not require extracting any anatomical landmarks. Third, the initial alignment is step-by-step refined by hierarchical surface registration. To evaluate the distance measures between lung boundary points, a three-dimensional distance map is generated by narrow-band distance propagation, which drives fast and robust convergence to the optimal value. Finally, correspondences of manually detected nodules are established from the pairs with the smallest Euclidean distances. Experimental results show that our segmentation method accurately extracts lung boundaries and the registration method effectively finds the nodule correspondences.     

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.     

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.     

2D to 3D fusion of echocardiography and cardiac CT for TAVR and TAVI image guidance

This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis “Mercedes Benz” sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.     

基于人工智能的多模态影像辅助海马体自动勾画研究

目的:利用基于深度学习的人工智能算法,结合头颅MRI和CT的多模态影像,开发海马结构自动勾画技术,为头颅放疗过程中海马体的保护提供高效、准确的自动勾画方法。方法:收集清华大学第一附属医院放疗科从2020年1月~12月就诊的40例脑转移癌患者的定位头颅CT及MRI影像,分别在CT图像、CT-MRI配准图像的两个数据集上训练3D U-Net、3D U-Net Cascade、3D BUC-Net 3个深度学习模型,计算3个模型自动分割的左右海马体与对应的人工标注之间的Dice相似系数（DSC）和95%豪斯多夫距离（95 HD）,以及两者的体积作为模型的分割准确性的评估,并且以对同一大小patch图像的自动分割耗时作为模型效率的评估。结果:引入MRI图像信息对左右海马的自动分割精度有明显的提升;模型3D BUC-Net在CT-MRI数据集上对左右海马体的自动分割都取得最好分割结果（DSC:0.900±0.017,0.882±0.026;95HD:0.792±0.084,0.823±0.093）,而且该模型的分割效率更高。结论:模型3D BUC-Net能在多模态影像上实现高效、准确的海马区的自动勾画,为头颅放疗过程中海马区的保护提供方便。     

启发式牙颌CT影像自动分割

准确快速地分割CT切片特征轮廓是医学图像三维重建的重要环节。现有的轮廓分割方法必须通过手动层层交互操作,不仅耗时而且分割精度不高。针对这种局限性,提出一种基于启发式牙颌CT影像自动分割方法。首先用拉普拉斯算子对CT图像序列进行边缘增强,其次用轮廓匹配映射技术实现轮廓启发式传递,最后基于收缩包围算法自动分割牙颌序列。以14例完整牙（每例28～32颗牙数据样本）锥束CT断层扫描图像序列进行实验,在相同条件下分别用所提出的轮廓自动提取方法和其他提取方法,对实验样本进行轮廓提取,得到单颗牙轮廓提取的平均用时和提取轮廓与真实轮廓之间的距离差平均值。实验结果显示,轮廓自动分割算法提取单颗牙轮廓的用时约为其他手工分割法提取单颗牙轮廓用时的23%,同时提取的轮廓质量和用传统方法提取的轮廓质量相当。该方法为CT数据特征区自动化分割提供一种可行且高效的方法,为进一步改进现有的CT影像分割和三维重建算法提供了新的思路。     

Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection

We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DEDR). CT is an established tool for the detection of cardiac calcification. DEDR could be a cost-effective alternative screening tool. In order to utilize CT as the "gold standard" to evaluate the capability of DEDR images for the detection and localization of calcium, we developed an automatic, intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DEDR images. To generate digitally reconstructed radiography (DRR) from the CT volumes, we developed several projection algorithms using the fast shear-warp method. In particular, we created a Gaussian-weighted projection for this application. We used normalized mutual information (NMI) as the similarity measurement. Simulated projection images from CT values were fused with the corresponding DEDR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with a translation difference of less than 0.8 mm and a rotation difference of less than 0.2 degrees. For physical phantom images, the registration accuracy is 0.43 +/- 0.24 mm. Color overlay and 3D visualization of clinical images show that the two images registered well. The NMI values between the DRR and DEDR images improved from 0.21 +/- 0.03 before registration to 0.25 +/- 0.03 after registration. Registration errors measured from anatomic markers decreased from 27.6 +/- 13.6 mm before registration to 2.5 +/- 0.5 mm after registration. Our results show that the automatic 3D-to-2D registration is accurate and robust. This technique can provide a useful tool for correlating DEDR with CT images for screening coronary artery calcification.     

基于点信息的人体寰椎三维模型配准

目的建立一种基于点信息的寰椎三维模型局部点配准方法,为进行三维数据的统计建模奠定基础。方法以正常人体CT序列图像生成寰椎三维模型30个,所有模型标注人工选择的对应点,设为模板模型1个,训练模型20个,验证模型9个。首先进行训练组模型对模板模型的配准,包括点信息的比较计算和权重系数的机器训练两步,以自动配准点与人工选点的欧式距离之和为测度,获得点配准公式及对应的最佳系数;其次以验证组模型对模板模型进行配准,统计自动配准点与人工选点的欧式距离值,同训练组做对比分析,评估方法的稳定性。结果获得配准函数及对应的最佳权重系数,训练组和验证组配准结果误差分别为1.983和2.045 mm,统计分析表明两组结果没有显著性差异。结论此方法精度及稳定性均达到预期目的,可用于寰椎模型之间感兴趣点的自动配准及统计建模工作中的元素分类。