Robust 2-D-3-D Registration Optimization for Motion Compensation During 3-D TRUS-Guided Biopsy Using Learned Prostate Motion Data
Robust 2-D-3-D Registration Optimization for Motion Compensation During 3-D TRUS-Guided Biopsy Using Learned Prostate Motion Data
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DOI：
10.1109/tmi.2017.2703150
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发表时间：
2017-10-01
影响因子：
10.6
通讯作者：
Ward, Aaron D.
中科院分区：
文献类型：
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作者：
De Silva, Tharindu;Cool, Derek W.;Ward, Aaron D.
在磁共振（MR）靶向、3-D经直肠超声（TRUS）引导的活检中，手术期间的前列腺运动增加了针靶向误差，并限制了准确采样MR可疑肿瘤体积的能力。用于前列腺运动补偿的2-D-3-D配准方法的鲁棒性受到搜索空间中的局部最优的影响。在本文中，我们分析了前列腺的运动特性，并研究了将这些知识纳入配准优化框架，以提高对局部最优的鲁棒性的方法。刚性运动的前列腺进行了分析，采用混合高斯（MoG）模型，使用3-D TRUS图像采集的双边六分仪探头位置与机械辅助活检系统。通过设计多个初始搜索位置和初始搜索方向，将学习到的运动特征纳入Powell的方向设置方法。在临床活检过程中采集的数据集上进行实验，并使用目标配准误差（TRE）和优化后的收敛图像相似性度量值来评估配准误差。将学习的初始化位置和方向纳入Powell方法后，进行2-D-3-D配准以补偿前列腺活检期间的运动，rms +/- std TRE为2.33 +/- 1.09 mm，每次配准的平均执行时间类似于3 s。这比Powell标准方法中观察到的3.12 +/- 1.70 mm有所改善。对于在临床协议下采集的数据，收敛图像相似性度量值在>= 8%的配准中得到改善，而仅在
In magnetic resonance (MR)-targeted, 3-D transrectal ultrasound (TRUS)-guided biopsy, prostate motion during the procedure increases the needle targeting error and limits the ability to accurately sample MR-suspicious tumor volumes. The robustness of the 2-D-3-D registration methods for prostate motion compensation is impacted by local optima in the search space. In this paper, we analyzed the prostate motion characteristics and investigated methods to incorporate such knowledge into the registration optimization framework to improve robustness against local optima. Rigid motion of the prostate was analyzed adopting a mixture-of-Gaussian (MoG) model using 3-D TRUS images acquired at bilateral sextant probe positions with a mechanically assisted biopsy system. The learned motion characteristics were incorporated into Powell's direction set method by devising multiple initial search positions and initial search directions. Experiments were performed on data sets acquired during clinical biopsy procedures, and registration error was evaluated using target registration error (TRE) and converged image similarity metric values after optimization. After incorporating the learned initialization positions and directions in Powell's method, 2-D-3-D registration to compensate for motion during prostate biopsy was performed with rms +/- std TRE of 2.33 +/- 1.09 mm with similar to 3 s mean execution time per registration. This was an improvement over 3.12 +/- 1.70 mm observed in Powell's standard approach. For the data acquired under clinical protocols, the converged image similarity metric value improved in >= 8% of the registrations whereas it degraded only