直肠癌术前放疗危及器官与临床靶区自动勾画的可行性研究

目的:探讨AccuContour软件及定制化自动勾画模型在直肠癌术前容积旋转调强放疗中临床靶区（CTV）和危及器官（OAR）自动勾画几何轮廓及剂量学各项参数精度,为临床应用提供依据。方法:回顾性选取133例已接受直肠癌术前容积旋转调强放疗的患者,随机分组,65例作为训练集,16例作为验证集,52例作为测试集,构建并训练自动勾画模型,将其导入AccuContour软件并自动勾画CTV和4个OAR,对比自动勾画与手动勾画在CTV和OAR几何轮廓的体积差异（[ΔV]）、Dice相似性系数（DSC）、Jaccard系数（JAC）、敏感性指数（SI）、包容性系数（lncl）、质心偏差（DC）、Hausdorff距离（HD）等,以及自动勾画与手动勾画CTV和OAR在同一容积旋转调强计划中所受照射剂量学参数差异,从而评估自动勾画效果。结果:CTV的DSC值、JAC值、SI值、lncl值为:0.84±0.06、0.72±0.08、0.81±0.07、0.87±0.08,[ΔV]值、DC值、HD值为:10.93%（4.56%, 15.37%）、5.03（3.27, 8.77） mm、15.03（15.00, 24.70） mm;OAR的DSC值、SI值、lncl值、JAC值、[ΔV]值、DC值、HD值比较优劣顺序依次为:右股骨头、左股骨头、膀胱、小肠;自动勾画与手工勾画剂量学参数对比中,除膀胱V30、小肠Dmean、CTV D95的差异有统计学意义外（P<0.05）,其余均无统计学意义（P>0.05）。结论:在直肠癌术前容积旋转调强放疗中,本研究所采用的自动勾画系统,对于CTV和OAR的自动勾画有一定准确性,为临床医生节省大量时间,提高工作效率。     

RT-Mind自动勾画技术应用于鼻咽癌放射治疗可行性研究

目的:探讨RT-Mind软件在鼻咽癌放疗临床靶区（CTV）、危及器官（OARs）自动勾画的可行性,为临床应用提供依据。方法:回顾性选取28例应用调强技术治疗的鼻咽癌患者,将放疗医师手动勾画CTV、OARs（脑干、脊髓、左右晶体、视交叉、左右视神经、左右腮腺、左右颞叶、左右颞颌关节、下颌骨）做为参考标准,再使用RT-Mind软件自动勾画CTV及OARs。对比手动与自动勾画在CTV和OARs区域的Dice相似性系数（DSC）、Jaccard系数（JAC）、敏感性指数（SI）、包容性系数（lncI）、质心偏差（DC）、Hausdorff距离（HD）等参数,从而评估自动勾画效果。结果:CTV的DSC、JAC、SI、lncl、DC、HD分别为:0.78±0.04、0.70±0.05、0.85±0.08、0.87±0.04、（7.76±5.03） mm、（12.3±1.16） mm,OARs中DC、HD值均在1 cm之内。结论:RT-Mind软件能够基本满足临床要求,能够较为准确地实现鼻咽癌患者CTV和OARs的自动勾画。由于病人既往病史的个体差异,放射治疗医师必须根据临床需要,对自动勾画的CTV和OARs进行修改后,才能用于治疗。若依据临床需求进一步完善自动勾画的个性化定制,相信RT-Mind软件在解剖结构复杂的鼻咽癌放疗中能够辅助放疗医生提高工作效率,更好地为患者服务。     

智能放疗云平台自动勾画食管癌患者心脏结构的应用

【摘要】目的:测试和评估智能放疗云平台（RAIC.OIS）在食管癌患者心脏结构自动勾画中的应用。方法:选取2018年2月~11月收治的20例食管癌患者进行研究。首先,将20例患者的放疗定位CT图像从Eclipse治疗计划系统传输至连心医疗的智能放疗云平台（RAIC.OIS）;然后,使用RAIC.OIS的自动勾画工具,对CT图像中的心脏结构进行自动勾画;最后,将勾画好的结构文件传输并导入Eclipse。通过比较自动勾画和手工勾画的体积差异、位置差异、形状一致性和勾画时间,评估该软件的自动勾画工具应用于心脏结构自动勾画的可行性。结果:根据测量的数据结果,发现有1例患者的心脏形状和位置比较特殊,排除该患者的数据,对余下19例患者的数据进行统计分析。自动和手工两种方式勾画食管癌患者心脏结构的体积差异为（-17.08±8.66）%,相似性指数值为0.87±0.05。x、y和z这3个方向的位置差异分别为（0.12±0.09）、（0.11±0.08）和（0.22±0.16） cm,总位置差异为（0.31±0.14） cm。19例患者的自动勾画时间为（83±12） s,手工勾画时间为（284±58） s。结论:智能放疗云平台的自动勾画工具,对绝大部分食管癌患者的心脏勾画能够达到满意的结果。使用该工具可缩短心脏结构的勾画时间,提高放疗工作效率。     

乐园化引导干预可提升儿童放疗摆位精度

目的:探讨乐园化引导干预对儿童放疗摆位精度的影响。方法:选取2020年3月～2022年5月在中山大学肿瘤防治中心放疗的儿童患者作为研究对象,按照是否参与乐园化引导干预分为试验组（24例）和对照组（21例）。试验组儿童患者在每次放疗前进行儿童乐园化诱导心理干预,待儿童患者完成心理适应后进行治疗;对照组实施常规放疗准备后治疗。比较两组儿童患者放疗实施的摆位精度。结果:试验组在左右（LR）、头脚（SI）、腹背（AP）方向的摆位误差分别为（-0.32±2.18）、（-0.12±2.24）、（-0.17±2.32） mm,对照组分别为（-0.93±1.91）、（0.79±1.75）、（-0.63±1.97） mm。两组摆位误差比较,在LR和SI方向的差异有统计学意义（LR:t=2.28, P=0.02;SI:t=-2.58, P=0.01）,而AP方向的差异无统计学意义（LR:t=1.63, P=0.11）。结论:乐园化引导干预可以提高儿童患者放疗的依从性,进而提高放疗的摆位精度,具有显著的临床应用意义和推广价值。     

基于Calypso电磁实时跟踪系统的4D剂量验证

目的:研究基于Calypso电磁实时跟踪系统的4D剂量验证的可行性,评估Calypso引导对运动靶区放疗剂量精度提高的有效性。方法:将5 cm×5 cm、10 cm×10 cm方野、直径d=10 cm圆形野以及5例IMRT、5例VMAT放疗计划移植到Delta4三维剂量验证系统,使用自主呼吸运动平台搭载Delta4模体进行SI方向上周期(T)=5 s,振幅(A)=±10 mm的往复运动,分别比较静态、动态无跟踪,以及使用Calypso实时跟踪系统运动阈值分别为±2、±3、±5 mm情况下Delta4实测的剂量分布和治疗计划系统剂量分布。结果:静态、动态测量Calypso运动阈值为±2、±3、±5 mm以及动态无跟踪时计划γ通过率的平均值分别为(97.5±2.4)%、(95.9±2.8)%、(93.9±3.8)%、(86.2±8.6)%、(65.0±11.1)%;与静态射野平均γ通过率比较,使用Calypso动态运动阈值为±2、±3 mm时γ通过率差异无统计学意义(P0.05), Calypso动态运动阈值为±5 mm与动态无跟踪时γ通过率差异具有统计学差异(P0.05)。结论:对于胸腹部肿瘤患者,基于Calypso实时电磁跟踪,结合Delta4三维验证系统对运动靶区进行实时4D剂量验证是可行的,使用Calypso跟踪运动肿瘤放疗时,剂量精度有明显提高。     

人工智能云技术在乳腺癌患者心脏亚结构自动勾画中的应用

目的:评估人工智能云技术勾画平台（AI Contour）在乳腺癌患者心脏亚结构自动勾画中的准确性和可行性。方法:选取10例进行乳腺癌放射治疗患者的血管增强CT作为研究对象。在AI Contour上分别采用手动勾画、自动勾画和自动勾画后手动修改模式来完成10例患者的心脏亚结构勾画,包括左心房、右心房、左心室、右心室。比较Dice相似性系数（DSC）、Jaccard系数（JC）、Hausdorf距离（HD）、质心偏差（CMD）、包容性系数（IncI）、敏感性指数（SI）、勾画时间。结果:以手动勾画为金标准,自动勾画与手动勾画各心脏亚结构的DSC>0.8,JC>0.6,HD<9 mm,CMD<5 mm,IncI>0.8,SI>0.7。自动勾画后手动修改进一步提高了勾画精度,其中JC>0.8。自动勾画时间与手动勾画时间为（85.50±6.06） s vs （1 160.30±74.31） s,差异具有统计学意义（P<0.05）。自动勾画后手动修改总时间与手动勾画时间为（558.70±33.40） s vs （1 160.30±74.31） s,差异具有统计学意义（P<0.05）。结论:通过比较发现自动勾画技术能以较高的精度完成乳腺癌患者左心房、右心房、左心室、右心室的勾画,节省了大量时间,自动勾画后手动修改能进一步提高各心脏亚结构的勾画精度,同时云勾画平台具有远程协作的优势,值得推广运用。     

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。     

光学表面监测系统自动摆位功能对乳腺癌放疗患者治疗精度及摆位时间的影响

目的:研究放射治疗摆位时，相较于以传统体表标记实行摆位，利用光学表面监测系统（OSMS）自动摆位功能实行摆位对乳腺癌放疗患者治疗精度及摆位时间的影响。方法:30例乳腺癌保乳术后患者随机分为两组，每组15例。OSMS组，以OSMS引导放疗摆位，利用六维自动移床摆位功能。体表标记组，以体表标记引导放疗摆位。记录每次摆位时间和CBCT配准误差数据。误差数据包括左右（x）、头脚（y）、前后（z）方向平移误差和旋转方向（Rx、Ry、Rz）误差。分别对两组CBCT配准误差数据和摆位时间数据行独立样本t检验。结果:OSMS组x、y、z方向平移误差和Rx、Ry、Rz方向旋转误差分别为（0.12±0.11）、（0.12±0.09）、（0.13±0.08） cm和0.33°±0.43°、0.56°±0.50°、0.50°±0.52°，体表标记组分别为（0.15±0.11）、（0.22±0.16）、（0.25±0.16） cm和0.66°±0.72°、0.99°±0.69°、0.78°±0.56°。两种摆位方法在平移y和z方向以及旋转Rx、Ry、Rz方向统计结果有统计学意义（P<0.01）。在x方向平移误差无统计学差异（P>0.05）。OSMS组平移误差绝对值≤0.3 cm在x、y、z方向分别为94%、97%、97%，体表标记组为91%、63%、60%；OSMS组旋转误差绝对值≤1°在Rx、Ry、Rz方向分别为88%、78%、82%，体表标记组为74%、53%、67%。OSMS组摆位时间为（130±27） s，体表标记组为（202±31） s，结果有统计学差异（P<0.01），OSMS引导自动摆位可减少约70 s摆位时间。结论:相较于体表标记引导摆位，利用OSMS自动摆位功能可明显提高摆位精度，减少摆位时间。 【关键词】乳腺癌；光学表面监测系统；放射治疗；自动摆位；体表标记     

基于四维CT和TumorLoc软件测定肺下叶呼吸动度

目的:利用四维CT(4D-CT)和Tumor Loc软件,研究肺下叶(右膈肌层面)距离脊柱不同位置处的呼吸动度。方法:采用放疗专用Philips Brilliance 24排大孔径CT定位机对10例行真空垫固定的患者进行4D-CT模拟定位扫描,将每个呼吸周期的CT图像平均分为10个呼吸时相。通过Tumor Loc软件打开每例患者的10个呼吸时相图像,获得肺下叶内(右膈肌层面)距离脊柱40、50、60、70、80、90 mm处血管中心点在三维方向的位移,分析位移变化及左右距离脊柱相同距离位置处三维方向的相关性。结果:左肺下叶(右膈肌层面),距离脊柱40、50、60、70、80、90 mm位置处,呼吸动度在Z方向(头脚)分别为(9.5±2.5)mm、(9.7±2.6)mm、(9.5±2.5)mm、(9.3±2.3)mm、(9.7±2.5)mm、(9.5±2.6)mm;右肺下叶(右膈肌层面),距离脊柱40、50、60、70、80、90 mm位置处,呼吸动度在Z方向(头脚)分别为(10.5±2.7)mm、(11.4±3.1)mm、(11.3±3.2)mm、(11.5±3.0)mm、(11.6±4.0)mm、(11.7±4.3)mm;左右相同距离位置处,X方向(左右)、Y方向(前后)差异无统计学意义(P0.05)。左右相同距离位置处,Z方向(头脚)在40、50、60 mm处差异有统计学意义(P分别为0.005、0.007、0.005);Z方向在70、80、90 mm处差异无统计学意义(P0.05)。结论:应用4D-CT通过Tumor Loc软件可精确测量肺下叶(右膈肌层面)不同位置处在三维方向的呼吸运动度。     

探索中央型肺癌肿块位移的影响因素及其数学模型的建立

目的:应用模拟定位机观察呼吸动度,探索肺癌实体瘤位移的影响因素,为中央型肺癌靶区勾画提供参考资料。方法:采用X线模拟定位机透视肿块的运动,应用ViewletCam软件和Photoshop软件处理图像,测量患侧膈肌、体表标记物、实体瘤中心点的移动幅度。分析肿瘤中心点的位移与患侧膈肌、体表标记物、肺活量、呼吸周期及肿瘤最大直径之间的相关性。结果:肿瘤中心点的位移在X轴上,仅有6例可以测量,测量结果无统计学意义;在Y轴上的移动幅度是(0.84±0.44)cm,得到回归方程:Y=-0.228+0.45X1+0.139X2(X1为患侧膈肌,X2为呼吸周期);在Z轴上是(0.25±0.18)cm,得到回归方程:Z=1.136X(X为体表标记物)。结论:应用X线模拟定位机、Photoshop软件和ViewletCam软件,以GTV代替CTV,精确地测量了肿瘤中心点的移动幅度,并建立Y轴、Z轴上的线性回归方程,为勾画ITV提供一种简单而且快捷的方法。     

Tumor tracking and motion compensation with an adaptive tumor tracking system (ATTS): system description and prototype testing

A novel system for real-time tumor tracking and motion compensation with a robotic HexaPOD treatment couch is described. The approach is based on continuous tracking of the tumor motion in portal images without implanted fiducial markers, using the therapeutic megavoltage beam, and tracking of abdominal breathing motion with optical markers. Based on the two independently acquired data sets the table movements for motion compensation are calculated. The principle of operation of the entire prototype system is detailed first. In the second part the performance of the HexaPOD couch was investigated with a robotic four-dimensional-phantom capable of simulating real patient tumor trajectories in three-dimensional space. The performance and limitations of the HexaPOD table and the control system were characterized in terms of its dynamic behavior. The maximum speed and acceleration of the HexaPOD were 8 mm/s and 34.5 mm/s2 in the lateral direction, and 9.5 mm/s and 29.5 mm/s2 in longitudinal and anterior-posterior direction, respectively. Base line drifts of the mean tumor position of realistic lung tumor trajectories could be fully compensated. For continuous tumor tracking and motion compensation a reduction of tumor motion up to 68% of the original amplitude was achieved. In conclusion, this study demonstrated that it is technically feasible to compensate breathing induced tumor motion in the lung with the adaptive tumor tracking system.     

Feasibility of intrafraction whole-body motion tracking for total marrow irradiation

With image-guided tomotherapy, highly targeted total marrow irradiation (TMI) has become a feasible alternative to conventional total body irradiation. The uncertainties in patient localization and intrafraction motion of the whole body during hour-long TMI treatment may pose a risk to the safety and accuracy of targeted radiation treatment. The feasibility of near-infrared markers and optical tracking system (OTS) is accessed along with a megavoltage scanning system of tomotherapy. Three near-infrared markers placed on the face of a rando phantom are used to evaluate the capability of OTS in measuring changes in the markers' positions as the rando is moved in the translational direction. The OTS is also employed to determine breathing motion related changes in the position of 16 markers placed on the chest surface of human volunteers. The maximum uncertainty in locating marker position with the OTS is 1.5 mm. In the case of normal and deep breathing motion, the maximum marker position change is observed in anterior-posterior direction with the respective values of 4 and 12 mm. The OTS is able to measure surface changes due to breathing motion. The OTS may be optimized to monitor whole body motion during TMI to increase the accuracy of treatment delivery and reduce the radiation dose to the lungs.     

Multiple template-based fluoroscopic tracking of lung tumor mass without implanted fiducial markers

Precise lung tumor localization in real time is particularly important for some motion management techniques, such as respiratory gating or beam tracking with a dynamic multi-leaf collimator, due to the reduced clinical tumor volume (CTV) to planning target volume (PTV) margin and/or the escalated dose. There might be large uncertainties in deriving tumor position from external respiratory surrogates. While tracking implanted fiducial markers has sufficient accuracy, this procedure may not be widely accepted due to the risk of pneumothorax. Previously, we have developed a technique to generate gating signals from fluoroscopic images without implanted fiducial markers using a template matching method (Berbeco et al 2005 Phys. Med. Biol. 50 4481-90, Cui et al 2007 Phys. Med. Biol. 52 741-55). In this paper, we present an extension of this method to multiple-template matching for directly tracking the lung tumor mass in fluoroscopy video. The basic idea is as follows: (i) during the patient setup session, a pair of orthogonal fluoroscopic image sequences are taken and processed off-line to generate a set of reference templates that correspond to different breathing phases and tumor positions; (ii) during treatment delivery, fluoroscopic images are continuously acquired and processed; (iii) the similarity between each reference template and the processed incoming image is calculated; (iv) the tumor position in the incoming image is then estimated by combining the tumor centroid coordinates in reference templates with proper weights based on the measured similarities. With different handling of image processing and similarity calculation, two such multiple-template tracking techniques have been developed: one based on motion-enhanced templates and Pearson's correlation score while the other based on eigen templates and mean-squared error. The developed techniques have been tested on six sequences of fluoroscopic images from six lung cancer patients against the reference tumor positions manually determined by a radiation oncologist. The tumor centroid coordinates automatically detected using both methods agree well with the manually marked reference locations. The eigenspace tracking method performs slightly better than the motion-enhanced method, with average localization errors less than 2 pixels (1 mm) and the error at a 95% confidence level of about 2-4 pixels (1-2 mm). This work demonstrates the feasibility of direct tracking of a lung tumor mass in fluoroscopic images without implanted fiducial markers using multiple reference templates.     

Extension of a data-driven gating technique to 3D, whole body PET studies

Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.     

光学表面成像系统实时运动监测精度研究

目的:探究光学表面成像系统实时运动监测的精度。方法:将30例患者的呼吸曲线输入到模体中模拟呼吸运动， 同时利用Catalyst系统对模体进行实时运动监测，比较系统监测的呼吸曲线与参考曲线，从而得到光学表面成像系统实时 运动监测的精度。结果:光学表面成像系统监测的呼吸曲线与参考曲线具有较高的一致性，相关系数均大于0.99，显著相 关。监测误差的平均值为（0.24±0.04）mm，并且随着呼吸信号频率的增加而减小。结论:光学表面成像系统的实时运动 监测精度较高，可用于对患者呼吸运动的监测。在进行呼吸门控治疗时，应考虑呼吸监测系统引入的误差。     

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.     

Analysis of cardiac ventricular wall motion based on a three-dimensional electromechanical biventricular model

This paper describes a biventricular model, which couples the electrical and mechanical properties of the heart, and computer simulations of ventricular wall motion and deformation by means of a biventricular model. In the constructed electromechanical model, the mechanical analysis was based on composite material theory and the finite-element method; the propagation of electrical excitation was simulated using an electrical heart model, and the resulting active forces were used to calculate ventricular wall motion. Regional deformation and Lagrangian strain tensors were calculated during the systole phase. Displacements, minimum principal strains and torsion angle were used to describe the motion of the two ventricles. The simulations showed that during the period of systole, (1) the right ventricular free wall moves towards the septum, and at the same time, the base and middle of the free wall move towards the apex, which reduces the volume of the right ventricle; the minimum principle strain (E3) is largest at the apex, then at the middle of the free wall and its direction is in the approximate direction of the epicardial muscle fibres; (2) the base and middle of the left ventricular free wall move towards the apex and the apex remains almost static; the torsion angle is largest at the apex; the minimum principle strain E3 is largest at the apex and its direction on the surface of the middle wall of the left ventricle is roughly in the fibre orientation. These results are in good accordance with results obtained from MR tagging images reported in the literature. This study suggests that such an electromechanical biventricular model has the potential to be used to assess the mechanical function of the two ventricles, and also could improve the accuracy of ECG simulation when it is used in heart-torso model-based body surface potential simulation studies.     

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.     

融合块匹配和插值法的膈肌应变估计及分析

为了准确评估膈肌收缩功能，提出一种融合块匹配法和插值法的膈肌运动位移和应变估计方法。首先利用基于归一化互相关的块匹配法结合膈肌生理约束特性估计出膈肌感兴趣区域的互相关函数，对膈肌运动的整数位移进行估计；然后利用9点抛物线插值算法对互相关函数进行插值处理，进一步计算膈肌感兴趣区域二维亚像素位移；接下来根据估计出的位移计算应变；最后利用反追踪的思想选取最佳窗口的大小，以降低位移追踪误差。按照上述方法，分别针对吸气相超声膈肌全序列图像以及含吸气开始和吸气结束的首尾两张膈肌图像进行追踪，观察不同大小感兴趣区域窗口设置对膈肌运动位移和应变估计的影响。实验结果表明块匹配算法的窗口大小选择中，5×10和10×15窗口存在匹配错误，15×20、20×25、25×30、30×35窗口追踪得到的水平位移和竖直位移均无显著差异，而固定窗口大小为25×30最佳；选择使用全序列图像和首尾两张图像对膈肌全局应变计算不存在显著差异。     

Evaluation of residual patient position variation for spinal radiosurgery using the Novalis image guided system

PURPOSE: The Novalis system has been demonstrated to achieve accurate target localization on anthropomorphic phantoms. However, other factors, such as rotational deviation, patient intrafraction motion, and image fusion uncertainty due to patient body deformation, could contribute additional position uncertainty for actual patients. This study evaluates such position uncertainty for spinal radiosurgery patients. MATERIALS AND METHODS: Fifty-two consecutive spinal radiosurgery patients were included in the study. Rotational deviation was evaluated from 6-deg of freedom (6D) fusion results for all patients. The combined uncertainty of patient motion and image fusion was determined from fusion results of additional kV x-ray images acquired before, during, and after treatment for 25 of the 52 patients. The uncertainty of image fusion was also evaluated by performing 6D fusion ten different times with various regions of interest in the images selected for fusion. This was performed for two patients with L3 and T2 lesions, respectively, for comparison. RESULTS: The mean rotational deviation was 0.7 +/- 1.8, 0.7 +/- 1.5, and 0.7 +/- 1.6 deg along the yaw, roll, and pitch directions, respectively. The combined uncertainty from patient motion and image fusion was 0.1 +/- 0.9, 0.2 +/- 1.2, and 0.2 +/- 1.0 mm in the anteroposterior (AP), longitudinal, and lateral directions, respectively. The uncertainty (standard deviation) due to image fusion was less than 0.28 mm in any direction for the L3 lesion and 0.8 mm in the AP direction for the T2 lesion. CONCLUSION: Overall position uncertainty for spinal radiosurgery patients has been evaluated. Rotational deviation and patient motion were the main factors contributed to position uncertainty for actual patient treatment.