Acceleration and validation of optical flow based deformable registration for image-guided radiotherapy
Acceleration and validation of optical flow based deformable registration for image-guided radiotherapy
复制标题
DOI：
10.1080/02841860802258760
复制
发表时间：
2008-01-01
期刊：
影响因子：
3.1
通讯作者：
Sorensen, Thomas Sangild
中科院分区：
文献类型：
--
作者：
Noe, Karsten Ostergaard;De Senneville, Baudouin Denis;Sorensen, Thomas Sangild
材料与方法。基于光流估计的两种配准方法已被编程运行在图形编程单元（GPU）上。Horn Schunck的这些方法之一在具有10个相位和每个相位识别的41个标志的4DCT胸部数据集上进行测试。Cornelius Kanade的另一种方法在一系列六个3D锥形束CT（CBCT）数据集和头颈部癌症患者的常规计划CT数据集上进行了测试。在这些数据集中的每一个中，在颈椎和颅底上已经识别出6个标志点。执行CBCT到CBCT和CBCT到CT配准。结果对于4DCT配准，通过可变形配准将平均界标误差从3.5 +/-2.0 mm降低至1.1 +/-0.6 mm。对于CBCT到CBCT配准，刚性配准后的平均骨标志误差为1.8 +/-1.0 mm，变形配准后为1.6 +/-0.8 mm。对于CBCT到CT的配准误差，刚性配准和变形配准分别为2.2 +/- 0.6mm和1.8 +/- 0.6mm。使用GPU硬件，Horn Schunck方法被加速了48倍。4DCT配准可在37秒内完成。头颈部癌症患者登记需要64秒。讨论与限制标志点确定准确性的图像切片厚度相比，我们认为配准的标志点准确性是可接受的。与刚性配准相反，CBCT图像中识别出的点并未给出进行可变形配准的结果的完整印象。正在计划一项更大的确认研究，其中软组织标志将有助于跟踪可变形配准。使用GPU硬件获得的加速意味着可以在线完成CBCT配准。
Materials and methods. Two registration methods based on optical flow estimation have been programmed to run on a graphics programming unit (GPU). One of these methods by Horn Schunck is tested on a 4DCT thorax data set with 10 phases and 41 landmarks identified per phase. The other method by Cornelius Kanade is tested on a series of six 3D cone beam CT (CBCT) data sets and a conventional planning CT data set from a head and neck cancer patient. In each of these data sets 6 landmark points have been identified on the cervical vertebrae and the base of skull. Both CBCT to CBCT and CBCT to CT registration is performed. Results. For the 4DCT registration average landmark error was reduced by deformable registration from 3.5 +/- 2.0mm to 1.1 +/- 0.6mm. For CBCT to CBCT registration the average bone landmark error was 1.8 +/- 1.0mm after rigid registration and 1.6 +/- 0.8mm after deformable registration. For CBCT to CT registration errors were 2.2 +/- 0.6mm and 1.8 +/- 0.6mm for rigid and deformable registration respectively. Using GPU hardware the Horn Schunck method was accelerated by a factor of 48. The 4DCT registration can be performed in 37seconds. The head and neck cancer patient registration takes 64seconds. Discussion. Compared to image slice thickness, which limits accuracy of landmark point determination, we consider the landmark point accuracy of the registration acceptable. The points identified in the CBCT images do not give a full impression of the result of doing deformable registration as opposed to rigid registration. A larger validation study is being planned in which soft tissue landmarks will facilitate tracking the deformable registration. The acceleration obtained using GPU hardware means that registration can be done online for CBCT.