Yorke Afua A, Solis David, Guerrero Thomas
Department of Radiation Oncology, UW Medicine, Seattle, WA, USA.
Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA.
J Appl Clin Med Phys. 2020 Nov;21(11):14-22. doi: 10.1002/acm2.12965. Epub 2020 Oct 17.
Clinical image pairs provide the most realistic test data for image registration evaluation. However, the optimal registration is unknown. Using combinatorial rigid registration optimization (CORRO) we demonstrate a method to estimate the optimal alignment for rigid-registration of clinical image pairs.
Expert selected landmark pairs were selected for each CT/CBCT image pair for six cases representing head and neck, thoracic, and pelvic anatomic regions. Combination subsets of a k number of landmark pairs (k-combination set) were generated without repeat to form a large set of k-combination sets (k-set) for k = 4,8,12. The rigid transformation between the image pairs was calculated for each k-combination set. The mean and standard deviation of these transformations were used to derive final registration for each k-set.
The standard deviation of registration output decreased as the k-size increased for all cases. The joint entropy evaluated for each k-set of each case was smaller than those from two commercially available registration programs indicating a stronger correlation between the image pair after CORRO was used. A joint histogram plot of all three algorithms showed high correlation between them. As further proof of the efficacy of CORRO the joint entropy of each member of 30 000 k-combination sets in k = 4 were calculated for one of the thoracic cases. The minimum joint entropy was found to exist at the estimated mean of registration indicating CORRO converges to the optimal rigid-registration results.
We have developed a methodology called CORRO that allows us to estimate optimal alignment for rigid-registration of clinical image pairs using a large set landmark point. The results for the rigid-body registration have been shown to be comparable to results from commercially available algorithms for all six cases. CORRO can serve as an excellent tool that can be used to test and validate rigid registration algorithms.
临床图像对为图像配准评估提供了最真实的测试数据。然而，最佳配准情况未知。我们使用组合刚性配准优化（CORRO）方法展示了一种估计临床图像对刚性配准最佳对齐的方法。
为代表头颈部、胸部和骨盆解剖区域的6个病例的每个CT/CBCT图像对选择专家选定的地标点对。生成k个地标点对的组合子集（k组合集）且无重复，以形成大量的k组合集（k集），k分别为4、8、12。针对每个k组合集计算图像对之间的刚性变换。这些变换的均值和标准差用于得出每个k集的最终配准。
所有病例中，随着k值增大，配准输出的标准差减小。对每个病例的每个k集评估的联合熵小于两个商用配准程序的联合熵，表明使用CORRO后图像对之间的相关性更强。所有三种算法的联合直方图显示它们之间具有高度相关性。作为CORRO有效性的进一步证据，针对其中一个胸部病例计算了k = 4时30000个k组合集的每个成员的联合熵。发现最小联合熵存在于估计的配准均值处，表明CORRO收敛到最佳刚性配准结果。
我们开发了一种名为CORRO的方法，该方法允许我们使用大量地标点估计临床图像对刚性配准的最佳对齐。在所有六个病例中，刚体配准的结果已显示与商用算法的结果相当。CORRO可作为测试和验证刚性配准算法的优秀工具。
