A feasibility study to estimate optimal rigid‐body registration using combinatorial rigid registration optimization (CORRO)

Abstract

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.

A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT

“…In this study, we collected experimental data of 58 patients that were assembled as training (46) and testing (9) data sets from TCIA (47)(48)(49). Cone-beam computed tomography (CBCT) and planning CT (PCT) images were acquired from each patient, and the number of axial slices of CBCT and PCT per patient is approximately 88 and 130, respectively.…”

Quality Assurance of Image Registration Using Combinatorial Rigid Registration Optimization (CORRO)

“…This result is shown in Table 2 and the pie chart results in Figure 2 and the linear plot in Figure 3. Joint entropy has been discussed fully in our previous publication [26].…”

“…In this study, we present an offline quality assurance technique for image registration using a curated and statistically characterized reference data set of pelvic cases, each consisting of expert placed landmark points, CORRO registration values, and the original CT/CBCT image pairs from radiation therapy (RT) patient set-up for pelvic cases. We employ a method of joint entropy to quantitatively measure CORRO as we discussed in [26].…”