Real-time tumor tracking using sequential kV imaging combined with respiratory monitoring: a general framework applicable to commonly used IGRT systems

被引:45
作者
Cho, Byungchul [1 ,2 ]
Poulsen, Per Rugaard [1 ,3 ]
Keall, Paul J. [1 ]
机构
[1] Stanford Univ, Dept Radiat Oncol, Stanford, CA 94305 USA
[2] Asan Med Ctr, Dept Radiat Oncol, Seoul, South Korea
[3] Aarhus Univ, Dept Oncol, Dept Med Phys, DK-8000 Aarhus C, Denmark
关键词
MOTION COMPENSATION; INTRAFRACTION MOTION; RADIOTHERAPY; ACCURACY; IMAGER; SIMULATION; MANAGEMENT;
D O I
10.1088/0031-9155/55/12/003
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Clinical image guided radiotherapy (IGRT) systems have kV imagers and respiratory monitors, the combination of which provides an 'internal-external' correlation for respiratory-induced tumor motion tracking. We developed a general framework of correlation-based position estimation that is applicable to various imaging configurations, particularly alternate stereoscopic (ExacTrac) or rotational monoscopic (linacs) imaging, where instant 3D target positions cannot be measured. By reformulating the least-squares estimation equation for the correlation model, the necessity to measure 3D target positions from synchronous stereoscopic images can be avoided. The performance of this sequential image-based estimation was evaluated in comparison with a synchronous image-based estimation. Both methods were tested in simulation studies using 160 abdominal/thoracic tumor trajectories and an external respiratory signal dataset. The sequential image-based estimation method (1) had mean 3D errors less than 1 mm at all the imaging intervals studied (0.2, 1, 2, 5 and 10 s), (2) showed minimal dependencies of the accuracy on the geometry and (3) was equal in accuracy to the synchronous image-based estimation method when using the same image input. In conclusion, the sequential image-based estimation method can achieve sub-mm accuracy for commonly used IGRT systems, and is equally accurate and more broadly applicable than the synchronous image-based estimation method.
引用
收藏
页码:3299 / 3316
页数:18
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