基于Cone Beam CT图像的呼吸运动分析

呼吸运动是有一定规律性的运动,传统呼吸运动模型用公式描述,不能准确反映不同病人的特点或同一病人不同时期的特点,无法满足实时准确分析的需要.为此,我们提出了一种通过跟踪病人自由呼吸状态下所采集的Cone Beam CT图像序列中的横隔膜的运动来建立病人呼吸运动模型的方法.该方法建立的模型与传统的呼吸运动理论模型非常相似,证明了它是可行且有效的,同时该方法更能实时准确地反映病人的呼吸运动规律,具有很高的临床实用价值.     

基于双目视觉的呼吸运动实时跟踪方法研究

探讨一种基于双目视觉的呼吸运动实时跟踪方法,以减少在胸腹部的肿瘤放射治疗中由于呼吸等因素造成肿瘤位置动态位移而引起的治疗误差。使用由双摄像机组成的计算机视觉系统,实时匹配出标记物在左右两摄像机采集的图像中的具体坐标,依据双目成像的基本原理,计算出标记物在腹部表面的三维坐标值,结合时间参数,计算出该特征点三维坐标的变化情况,以此来完成对呼吸运动的实时跟踪。在目标跟踪过程中,使用鲁棒性强的SIFT(scale invariant feature transform)算法作为目标图像匹配的方法,并且在算法设计过程中,采用动态选择待匹配图像和局部搜索的策略。实验结果表明,目标图像匹配精确,减少了提取图像SIFT特征所需要的大量时间。在一个呼吸周期内,视觉测量的标记物的最大运动范围与实际测量值不超过0.2 cm,且能够做到实时性计算。该模型的运行精度高,能够较好地实时跟踪人体的呼吸运动。     

基于红外定位系统三维超声的研究

目的通过引入全数字的实时红外定位系统,同时采集二维B超图像,重建三维超声系统,来实时跟踪由于呼吸运动和组织变形导致的靶区的运动。方法实验主要通过在二维B超探头上安装3个红外定位小球(非等边),实时跟踪探头的位置,将二维超声图像映射到三维空间,然后通过三棱锥模型多次采集图像测量其几何位置关系,结合B超图像的性质,建立三维超声膜体数据,实验以立方体模型作为实验膜体,同时以水槽作为实验环境。结果实验证明二维超声及红外定位系统可以快速重建三维超声,重建膜体的三条棱锥形状及方向与实际的膜体相符合,重建精度准确,快速便捷。结论实验提出的三维超声重建技术,采集二维超声图像同时结合实时跟踪的红外定位设备,可以快速方便地实现定标及三维超声的建立,并且具有较高的重建准确性。     

基于双目视觉的呼吸运动实时跟踪方法研究

探讨一种基于双目视觉实时监测和跟踪人体的呼吸运动情况,减少肿瘤靶区组织因呼吸等运动产生位移而引起的治疗误差,以实现在放疗过程中减少呼吸运动对精确放疗产生的影响。采用放置治疗床上方的双摄像机,实时采集带有标记物的图片传送给计算机,使用余弦算法对放置胸腹体表的标记物进行特征识别,对视差信息进行图片匹配,采用双目视觉和小孔成像原理计算标记物三维坐标,通过监测标记物随时间变化的具体坐标可获取标记物是否因呼吸等运动产生位移。实验中,实时测量9个标记物的三维坐标。实验结果表明,9个体表标记物的测量值与实际值之间的平均误差小于±1 mm,其标准误差值小于0.12 mm,且计算一次9个标记物三维坐标需要35 ms。基于双目视觉的呼吸运动跟踪是一种高精度、良好实时性与稳定性的跟踪方法,可以减少呼吸运动对精确放疗产生的影响。     

基于体表标志点的肿瘤呼吸运动跟踪方法

目的:探讨一种基于体表标志点的肿瘤呼吸运动跟踪方法,避免放疗过程中植入式的肿瘤跟踪对患者造成的损伤。方法:利用双目视觉系统对体模表面标志点进行实时跟踪,根据双目视觉原理,得出标志点的三维坐标运动情况;同时利用瓦里安微焦斑X线机和飞利浦移动式C臂组成的正交X线图像采集装置获取离散时刻下肿瘤图像,通过两幅图像肿瘤的二维坐标计算出其三维坐标;再采用粒子群优化(PSO)后的最小二乘支持向量机(LSSVM)方法拟合出任意时刻肿瘤的三维坐标,实现肿瘤的实时定位与跟踪。结果:双目视觉系统能够准确计算出标志点的三维坐标,并能实时跟踪体表标志点;LSSVM-PSO避免了常规LSSVM交叉验证选取参数方法带来的盲目性,得到最优拟合参数,拟合出任意时刻下肿瘤坐标值与实际值之间不超过1.8 mm,拟合精度高,能够很好跟踪肿瘤运动。结论:基于体表标志点的肿瘤跟踪是一种很好的替代植入式跟踪方法,可以减少呼吸运动对精确放疗产生的影响以及避免手术植入标志点造成不必要的损伤。     

基于运动跟踪获取人体呼吸曲线的初步研究

4D-CT的实现需要在采集CT图像的同时,同步获取患者的呼吸曲线,再据此对CT图像进行排序和三维重建.本研究提出一种基于运动跟踪获取人体呼吸曲线的方法.即在人体腹部贴上标记物,用两台摄像机在CT扫描过程中成正交位同步跟踪呼吸运动,将得到的视频提取为单帧图像,利用区域生长算法提取标记物的质心,并跟踪质心的运动,描绘坐标的任意维度与时间的关系曲线,即得到的呼吸曲线.通过此方法获得了正常人在深呼吸时的呼吸曲线,并在该曲线上确定了屏气模式下7个时相点的位置,腹部表面在z轴方向上的运动幅度最大,约为3.5 cm.实验结果表明该方法切实可行,并且相比于其他的呼吸信号提取方法,具有简单易行的优点.     

基于支持向量回归的呼吸运动预测技术的研究

胸腹部肿瘤放疗中,由于呼吸运动的影响需要对靶区进行实时跟踪以保证放疗精度,并通过预测来补偿系统延时。本研究提出一种基于支持向量回归的呼吸运动预测方法,该方法先选取一段呼吸运动序列进行训练得到回归模型,当有新的呼吸序列时,根据训练模型计算输出。并在此基础上,动态更新训练集,使模型在线更新,实现精确在线支持向量回归。实验中对7例呼吸运动样本数据分别用离线模型和在线模型进行训练并预测,平均绝对误差分别为0.42 mm和0.30 mm。在线精确支持向量回归能更准确刻画呼吸运动轨迹,拟合结果精度高,满足实际应用中的需求。     

介入式治疗中靶区呼吸运动实时跟踪技术

对靶区呼吸运动实时跟踪技术研究进展进行报道。首先阐述了传统的内外呼吸信号获取方法,详细比较目前呼吸信号提取方法,提出使用CO_2浓度获得呼吸运动信号的方法。随后主要研究了内外信号关联模型,对现阶段的关联模型进行分类,分析、比较了其优缺点。在仿真和实际应用中,研究者发现呼吸替代信号的精度很大程度上影响关联模型的精度,而简单的关联模型不能够描述复杂的呼吸运动,需要建立复杂关联模型对其进行更加精确地描述。最后分析靶区呼吸运动实时跟踪技术存在的问题以及未来发展趋势。     

基于后验概率的呼吸信号预测

放疗过程中,采用图像引导、呼吸门控或实时跟踪技术对受呼吸影响较大的胸腹部位肿瘤目标进行治疗时,需要对呼吸条件下目标的运动进行估计.呼吸运动具有不确定性,利用传统数学模型描述其变化规律时,无法有效处理该问题.本研究提出后验概率算法进行呼吸运动估计,并利用呼吸状态判别技术有效控制跟踪过程,以解决呼吸的非线性逼近和基线漂移等问题.实验通过对11例患者的呼吸运动进行预测,证实了所提出方法的有效性;在应对信号变化和延时等方面,后验概率估计与传统算法的比较,也取得了令人满意的效果.     

基于PET/CT探测环真光子数呼吸运动门控的研究

呼吸运动是导致PET/CT胸腹部成像质量下降的主要原因,探寻一种行之有效的方法来降低呼吸运动对PET/CT成像质量的影响,在临床上对疾病的诊疗显得尤其重要。提出一种基于探测环真光子数的呼吸运动门控方法来提高PET/CT胸腹部诊断图像质量,利用PET探测环真光子数分布会随着体模的运动而发生相应变化的特征来对原始数据进行呼吸运动门控处理。采用GATE软件仿真PET/CT成像的过程,分别使用几何体模和像素体模(NCAT)仿真肺部在PET/CT扫描过程中的运动。然后,使用提出的方法门控仿真扫描数据,并重建出三维图像。相对于运动模糊图像,门控图像目标区域的形状、大小和位置等方面更接近于静态图像。在冠状面上,门控图像质量优于运动模糊图像,其图像结构相似度(SSIM)分别提升5%、3%、9%;对于矢状面的line profiles,门控图像与静态图像接近,优于运动模糊图像。结果表明,所提出的方法能有效减少PET/CT图像的呼吸运动伪影,使肺部肿瘤的形状、大小和位置接近静态图像,并且有效地克服目前一些校正方法的局限性。     

A patient-specific respiratory model of anatomical motion for radiation treatment planning

The modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have developed a model of respiration-induced organ motion in the thorax without the commonly adopted assumption of repeatable breath cycles. The model describes the motion of a volume of interest within the patient based on a reference three-dimensional (3D) image (at end expiration) and the diaphragm positions at different time points. The input data are respiration-correlated CT (RCCT) images of patients treated for non-small- cell lung cancer, consisting of 3D images, including the diaphragm positions, at ten phases of the respiratory cycle. A deformable image registration algorithm calculates the deformation field that maps each 3D image to the reference 3D image. A principal component analysis is performed to parameterize the 3D deformation field in terms of the diaphragm motion. We show that the first two principal components are adequate to accurately and completely describe the organ motion in the data of four patients. Artifacts in the RCCT images that commonly occur at the mid-respiration states are reduced in the model-generated images. Further validation of the model is demonstrated in the successful application of the parameterized 3D deformation field to RCCT data of the same patient but acquired several days later. We have developed a method for predicting respiration-induced organ motion in patients that has potential for improving the accuracy of dose calculation in radiotherapy. Possible limitations of the model are cases where the correlation between lung tumor and diaphragm position is less reliable such as superiorly situated tumors and interfraction changes in tumor-diaphragm correlation. The limited number of clinical cases examined suggests, but does not confirm, the model's applicability to a wide range of patients.     

A novel respiratory detection method based on automated analysis of ultrasound diaphragm video

This paper proposes a novel respiratory detection method based on diaphragm motion measurements using a 2D ultrasound unit. The proposed method extracts a respiratory signal from an automated analysis of the internal diaphragm motion during breathing. The respiratory signal may be used for gating. Ultrasound studies of diaphragm breathing motion were performed on four volunteers. The ultrasound video stream was captured and transferred to a personal computer and decomposed into individual image frames. After straightforward image analysis, region of interest selection, and filtering, the mutual information (MI) and correlation coefficients (CCs) between a selected reference frame and all other frames were computed. The resulting MI and CC values were discovered to produce a signal corresponding to the respiratory cycle in both phase and magnitude. We also studied the diaphragm motion of two volunteers during repeated deep inspiration breath holds (DIBH) and found a slight relaxation motion of the diaphragm during the DIBH, suggesting that the residual motion may be important for treatments delivered at this breathing phase. Applying the proposed respiratory detection method to these ultrasound studies, we found that the MI and CC values demonstrate the relaxation behavior, indicatingthat our method may be used to determine the radiation triggering time for a DIBH technique.     

基于光学体表监控和X射线透视影像的膈肌运动自动跟踪

目的:基于直线加速器的光学体表监控系统和X射线透视影像利用人工智能构建膈肌顶点运动的自动跟踪模型。方法:同步采集7例肝肿瘤患者胸腹部的光学体表运动信息和千伏级X射线透视影像,选取其中3例患者数据利用主成分分析与偏最小二乘回归结合的方法计算不同体表感兴趣区域与膈肌运动的相关系数,选择相关系数最大的体表感兴趣区域作为光学体表监控区。首先,使用全卷积网络模型自动识别透视图像中膈肌顶点的位置;再利用随机森林方法建立体表与膈肌顶点运动的关联模型,基于体表运动信息实时预测膈肌顶点运动轨迹;最后,把自动跟踪的膈肌顶点位置与放疗医生手动勾画位置进行对比,以评估模型精度。结果:3例患者的体表感兴趣区域与膈肌运动的平均相关系数在前后（AP）方向最高达到（0.73±0.01） mm,上下（SI）方向最高达到（0.88±0.01） mm。自动跟踪模型预测结果与手动勾画位置的平均绝对误差和均方根误差SI方向分别为（3.09±0.79） mm和（3.89±0.89） mm,AP方向分别为（1.42±0.43） mm和（1.78±0.46） mm。结论:体表呼吸运动与体内膈肌运动是相关的,在放疗过程中基于光学体表运动信息可以实时跟踪体内膈肌顶点运动,该技术可用于胸腹部肿瘤放疗期间膈肌附近肿瘤的实时及无创运动管理。     

A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing

Breathing motion is a significant source of error in radiotherapy treatment planning for the thorax and upper abdomen. Accounting for breathing motion has a profound effect on the size of conformal radiation portals employed in these sites. Breathing motion also causes artifacts and distortions in treatment planning computed tomography (CT) scans acquired during free breathing and also causes a breakdown of the assumption of the superposition of radiation portals in intensity-modulated radiation therapy, possibly leading to significant dose delivery errors. Proposed voluntary and involuntary breath-hold techniques have the potential for reducing or eliminating the effects of breathing motion, however, they are limited in practice, by the fact that many lung cancer patients cannot tolerate holding their breath. We present an alternative solution to accounting for breathing motion in radiotherapy treatment planning, where multislice CT scans are collected simultaneously with digital spirometry over many free breathing cycles to create a four-dimensional (4-D) image set, where tidal lung volume is the additional dimension. An analysis of this 4-D data leads to methods for digital-spirometry, based elimination or accounting of breathing motion artifacts in radiotherapy treatment planning for free breathing patients. The 4-D image set is generated by sorting free-breathing multislice CT scans according to user-defined tidal-volume bins. A multislice CT scanner is operated in the ciné mode, acquiring 15 scans per couch position, while the patient undergoes simultaneous digital-spirometry measurements. The spirometry is used to retrospectively sort the CT scans by their correlated tidal lung volume within the patient's normal breathing cycle. This method has been prototyped using data from three lung cancer patients. The actual tidal lung volumes agreed with the specified bin volumes within standard deviations ranging between 22 and 33 cm3. An analysis of sagittal and coronal images demonstrated relatively small (<1 cm) motion artifacts along the diaphragm, even for tidal volumes where the rate of breathing motion is greatest. While still under development, this technology has the potential for revolutionizing the radiotherapy treatment planning for the thorax and upper abdomen.     

Motion compensated coronary interventional navigation by means of diaphragm tracking and elastic motion models

Current catheter tracking in the x-ray catheter laboratory during coronary interventions is performed using 2D fluoroscopy. Although this features real-time navigation on high-resolution images, drawbacks such as overlap and foreshortening exist and hamper the diagnosis and treatment process. An alternative to fluoroscopy-based tracking is device tracking by means of a magnetic tracking system (MTS). Having measured the 3D location of the interventional device, its position can be reconstructed on 3D images or virtual roadmaps of the organ or vessel structure under examination. In this paper, a method is presented which compensates the interventional device location measured by the MTS for organ motion and thus registers it dynamically to a 3D virtual roadmap. The motion compensation is accomplished by using an elastic motion model which is driven by the ECG signal and a respiratory sensor signal derived from ultrasonic diaphragm tracking. The model is updated during the intervention itself, thus allowing for a local refinement in regions which bear a complex geometric structure, such as stenoses and bifurcations. The evaluation is done by means of a phantom-based study using a dynamic heart-phantom. The mean displacement caused by the overall motion of the heart is improved from 10.4+/-4.8 mm in the uncompensated case to 2.1+/-1.2 mm in the motion compensated case.     

Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker

The aim of this work was to quantify the ability to predict intrafraction diaphragm motion from an external respiration signal during a course of radiotherapy. The data obtained included diaphragm motion traces from 63 fluoroscopic lung procedures for 5 patients, acquired simultaneously with respiratory motion signals (an infrared camera-based system was used to track abdominal wall motion). During these sessions, the patients were asked to breathe either (i) without instruction, (ii) with audio prompting, or (iii) using visual feedback. A statistical general linear model was formulated to describe the relationship between the respiration signal and diaphragm motion over all sessions and for all breathing training types. The model parameters derived from the first session for each patient were then used to predict the diaphragm motion for subsequent sessions based on the respiration signal. Quantification of the difference between the predicted and actual motion during each session determined our ability to predict diaphragm motion during a course of radiotherapy. This measure of diaphragm motion was also used to estimate clinical target volume (CTV) to planning target volume (PTV) margins for conventional, gated, and proposed four-dimensional (4D) radiotherapy. Results from statistical analysis indicated a strong linear relationship between the respiration signal and diaphragm motion (p<0.001) over all sessions, irrespective of session number (p=0.98) and breathing training type (p=0.19). Using model parameters obtained from the first session, diaphragm motion was predicted in subsequent sessions to within 0.1 cm (1 sigma) for gated and 4D radiotherapy. Assuming a 0.4 cm setup error, superior-inferior CTV-PTV margins of 1.1 cm for conventional radiotherapy could be reduced to 0.8 cm for gated and 4D radiotherapy. The diaphragm motion is strongly correlated with the respiration signal obtained from the abdominal wall. This correlation can be used to predict diaphragm motion, based on the respiration signal, to within 0.1 cm (1 sigma) over a course of radiotherapy.     

Three-dimensional prostate position estimation with a single x-ray imager utilizing the spatial probability density

In radiotherapy, target motion during treatment delivery can be managed either by motion inclusive margins or by gating or tracking based on intrafraction target position monitoring. If radio-opaque fiducial markers are used the required three-dimensional (3D) target position signal for gating or tracking can be obtained by simultaneous acquisition of two x-ray images from different angles. However, most treatment machines do not have such stereoscopic imaging capability. Alternatively, the 3D target position may be estimated with a single imager (monoscopic imaging) although it only provides the projected target position in the two dimensions of the imager plane. In this study, we developed a probability-based method to estimate the unresolved motion component parallel to the imager axis from the projected motion. A 3D Gaussian probability density was assumed for the target position. Projection of the target into a certain point on the imager means that it is located on the ray line that connects this point with the focus point of the x-ray source. The 1D probability density along this line was calculated from the 3D probability density and its expectation value was used as the estimate for the unresolved position. The mathematical framework of the method was developed including analytical expressions for the estimated unresolved component as a function of resolved components and for the estimation uncertainty. Use of the method was demonstrated for prostate in a simulation study of monoscopic imaging. First, the required 3D probability density was constructed as a population average from a data set consisting of 536 continuous prostate position tracks from 17 patients recorded at 10 Hz. Next, monoscopic imaging at a fixed imaging angle and imaging frequency was simulated for each prostate track. Estimated 3D prostate tracks were constructed from the simulated projection images by the proposed method and compared with the actual tracks in order to determine the root-mean-square (rms) error. The simulations were performed with imaging angles in the range from 0 degrees to 180 degrees (relative to vertical) and imaging frequencies in the range from 0.1 s (corresponding to continuous imaging) to 600 s (corresponding to no intrafraction imaging). For comparison, simulations were also performed with stereoscopic imaging, where perfect position determination in all three directions was assumed, and with monoscopic imaging without estimation of the unresolved motion, where the motion component along the imager axis was assumed to be zero. For continuous imaging, the accuracy of monoscopic imaging was limited by the uncertainty in the unresolved position estimation. The resulting vector rms error for the population corresponded closely to the theoretically derived estimation uncertainty. The estimation did not improve the accuracy of lateral monoscopic imaging, but it reduced the population rms error from 1.59 mm to 1.11 mm for vertical imaging. This improvement was most prominent for outlying tracks with large unresolved motion. Stereoscopic imaging was clearly superior to monoscopic imaging for high frequency imaging. For less frequent imaging, the accuracy of both monoscopic and stereoscopic imaging decreased due to target motion between images. Since this was most prominent for stereoscopic imaging, the difference in accuracy between monoscopic and stereoscopic imaging decreased with increasing imaging period. In conclusion, a method for estimation of the 3D target position from 2D projections has been developed and its use has been demonstrated in a simulation study of monoscopic prostate tracking.     

Evaluation of deformable image registration and a motion model in CT images with limited features

Deformable image registration (DIR) is increasingly used in radiotherapy applications and provides the basis for a previously described model of patient-specific respiratory motion. We examine the accuracy of a DIR algorithm and a motion model with respiration-correlated CT (RCCT) images of software phantom with known displacement fields, physical deformable abdominal phantom with implanted fiducials in the liver and small liver structures in patient images. The motion model is derived from a principal component analysis that relates volumetric deformations with the motion of the diaphragm or fiducials in the RCCT. Patient data analysis compares DIR with rigid registration as ground truth: the mean ± standard deviation 3D discrepancy of liver structure centroid positions is 2.0 ± 2.2 mm. DIR discrepancy in the software phantom is 3.8 ± 2.0 mm in lung and 3.7 ± 1.8 mm in abdomen; discrepancies near the chest wall are larger than indicated by image feature matching. Marker's 3D discrepancy in the physical phantom is 3.6 ± 2.8 mm. The results indicate that visible features in the images are important for guiding the DIR algorithm. Motion model accuracy is comparable to DIR, indicating that two principal components are sufficient to describe DIR-derived deformation in these datasets.     

Real-time 3D-surface-guided head refixation useful for fractionated stereotactic radiotherapy

Accurate and precise head refixation in fractionated stereotactic radiotherapy has been achieved through alignment of real-time 3D-surface images with a reference surface image. The reference surface image is either a 3D optical surface image taken at simulation with the desired treatment position, or a CT/MRI-surface rendering in the treatment plan with corrections for patient motion during CT/MRI scans and partial volume effects. The real-time 3D surface images are rapidly captured by using a 3D video camera mounted on the ceiling of the treatment vault. Any facial expression such as mouth opening that affects surface shape and location can be avoided using a new facial monitoring technique. The image artifacts on the real-time surface can generally be removed by setting a threshold of jumps at the neighboring points while preserving detailed features of the surface of interest. Such a real-time surface image, registered in the treatment machine coordinate system, provides a reliable representation of the patient head position during the treatment. A fast automatic alignment between the real-time surface and the reference surface using a modified iterative-closest-point method leads to an efficient and robust surface-guided target refixation. Experimental and clinical results demonstrate the excellent efficacy of <2 min set-up time, the desired accuracy and precision of <1 mm in isocenter shifts, and <1 degree in rotation.