Robertson Scott P, Weiss Elisabeth, Hugo Geoffrey D
Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, 23298.
Med Phys. 2014 Apr;41(4):041704. doi: 10.1118/1.4867860.
To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy.
Small (1 cm(3)), nonoverlapping image subvolumes ("blocks") were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4-7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration.
Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%-100%) in the initial bony alignment to 91% ± 8% (range: 56%-100%;p < 0.001). Left-right, anterior-posterior, and superior-inferior systematic BDE were 3.2, 2.4, and 4.4 mm, respectively, with random BDE of 2.4, 2.1, and 2.7 mm. Margins required to include both localization and delineation uncertainties ranged from 5.0 to 11.7 mm, an average of 40% less than required for bony alignment.
BMR is a promising approach for automatic lung tumor localization. Further evaluation is warranted to assess the accuracy and robustness of BMR against other potential localization strategies.
在图像引导放射治疗期间，实施并评估基于块匹配的配准（BMR）算法用于局部晚期肺癌肿瘤定位。
在计划图像上使用局部强度梯度度量自动识别小的（1 cm³）、不重叠的图像子体积（“块”）以覆盖肿瘤表面。使用刚性变换将块独立且自动地配准到治疗中的图像。为提高速度和稳健性，从粗到细的图像分辨率迭代执行配准。在每个分辨率下，存储所有具有接近最大相似性分数的块位移。从该列表中，迭代选择每个块的单个位移向量，该向量使紧邻块之间的位移向量一致性最大化。在进入更精细图像分辨率的配准之前，使用中值滤波器对这些选定的位移进行正则化。在评估所有图像分辨率之后，使用普罗克汝斯分析计算治疗中图像的全局刚性变换，为患者摆位校正提供治疗床位移。对18例局部晚期肺癌患者评估了该算法，每位患者有4 - 7次每周的治疗中计算机断层扫描，带有医生勾画的大体肿瘤体积。相对于名义上医生确定的靶区计算体积重叠（VO）和边界位移误差（BDE），以确定配准后的残余误差。
与仅使用全分辨率图像进行配准相比，多分辨率配准的实施使块匹配精度提高了39%。通过还考虑每个块的多个潜在位移，初始误差降低了65%。使用BMR算法的最终实施方案，VO从初始骨配准中的77%±21%（范围：0% - 100%）显著提高到91%±8%（范围：56% - 100%；p < 0.001）。左右、前后和上下方向的系统BDE分别为3.2、2.4和4.4 mm，随机BDE为2.4、2.1和2.7 mm。包括定位和勾画不确定性所需的边界范围为5.0至11.7 mm，平均比骨配准所需的边界少40%。
BMR是一种有前景的自动肺肿瘤定位方法。有必要进行进一步评估，以评估BMR相对于其他潜在定位策略而言的准确性和稳健性。
